CIRCULATE Galaxy Nakamura et al 2024
library(swimplot) library(Rmpfr) library(coxphf) library(grid) library(gtable) library(readr) library(mosaic) library(dplyr) library(survival) library(survminer) library(gridtext) library(ggplot2) library(scales) library(officer) library(ggthemes) library(tidyverse) library(gtsummary) library(flextable) library(parameters) library(car) library(grid) library(ComplexHeatmap) library(readxl) library(janitor) library(rms) library(DT)
#Demographics Table
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data_subset <- circ_data %>%
select(
Age,
Gender,
ECOG,
PrimSite,
pT,
pN,
Stage,
NAC,
ACT,
BRAF.V600E,
RAS,
MSI,
RFS.Event,
OS.months) %>%
mutate(
Age = as.numeric(Age),
Gender = factor(Gender, levels = c("Male", "Female")),
ECOG = factor(ECOG, levels = c(0, 1)),
PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
pT = factor(pT, levels = c("T1-T2", "T3-T4")),
pN = factor(pN, levels = c("N0", "N1-N2")),
Stage = factor(Stage, levels = c("I","II", "III", "IV")),
NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
MSI = factor(MSI, levels = c("MSS", "MSI-High")),
RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 2,2401 |
|---|---|
| Age | 69 (28 - 95) |
| Gender | |
| Male | 1,149 (51%) |
| Female | 1,091 (49%) |
| ECOG | |
| 0 | 2,046 (91%) |
| 1 | 194 (8.7%) |
| PrimSite | |
| Right-sided colon | 863 (39%) |
| Left-sided colon | 1,377 (61%) |
| Rectum | 0 (0%) |
| pT | |
| T1-T2 | 317 (16%) |
| T3-T4 | 1,630 (84%) |
| Unknown | 293 |
| pN | |
| N0 | 922 (47%) |
| N1-N2 | 1,025 (53%) |
| Unknown | 293 |
| Stage | |
| I | 234 (10%) |
| II | 652 (29%) |
| III | 936 (42%) |
| IV | 418 (19%) |
| NAC | |
| Neoadjuvant Chemotherapy | 218 (9.7%) |
| Upfront Surgery | 2,022 (90%) |
| ACT | |
| Adjuvant Chemotherapy | 946 (42%) |
| Observation | 1,294 (58%) |
| BRAF.V600E | |
| BRAF wt | 2,062 (92%) |
| BRAF V600E | 178 (7.9%) |
| RAS | |
| RAS wt | 1,303 (58%) |
| RAS mut | 937 (42%) |
| MSI | |
| MSS | 2,025 (90%) |
| MSI-High | 215 (9.6%) |
| RFS.Event | |
| Recurrence | 500 (22%) |
| No Recurrence | 1,740 (78%) |
| OS.months | 23 (2 - 49) |
| 1 Median (Range); n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)Warning: The `strip_md_bold` argument of `as_flex_table()` is deprecated as of gtsummary 1.6.0.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.fit1Characteristic | N = 2,2401 |
|---|---|
Age | 69 (28 - 95) |
Gender | |
Male | 1,149 (51%) |
Female | 1,091 (49%) |
ECOG | |
0 | 2,046 (91%) |
1 | 194 (8.7%) |
PrimSite | |
Right-sided colon | 863 (39%) |
Left-sided colon | 1,377 (61%) |
Rectum | 0 (0%) |
pT | |
T1-T2 | 317 (16%) |
T3-T4 | 1,630 (84%) |
Unknown | 293 |
pN | |
N0 | 922 (47%) |
N1-N2 | 1,025 (53%) |
Unknown | 293 |
Stage | |
I | 234 (10%) |
II | 652 (29%) |
III | 936 (42%) |
IV | 418 (19%) |
NAC | |
Neoadjuvant Chemotherapy | 218 (9.7%) |
Upfront Surgery | 2,022 (90%) |
ACT | |
Adjuvant Chemotherapy | 946 (42%) |
Observation | 1,294 (58%) |
BRAF.V600E | |
BRAF wt | 2,062 (92%) |
BRAF V600E | 178 (7.9%) |
RAS | |
RAS wt | 1,303 (58%) |
RAS mut | 937 (42%) |
MSI | |
MSS | 2,025 (90%) |
MSI-High | 215 (9.6%) |
RFS.Event | |
Recurrence | 500 (22%) |
No Recurrence | 1,740 (78%) |
OS.months | 23 (2 - 49) |
1Median (Range); n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#Table of Metastatic organ involvement in Stage IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Stage=="IV",]
circ_data_subset <- circ_data %>%
select(
Mets.Organ) %>%
mutate(
Mets.Organ = factor(Mets.Organ))
table1 <- circ_data_subset %>%
tbl_summary(
statistic = list(
all_continuous() ~ "{median} ({min} - {max})",
all_categorical() ~ "{n} ({p}%)")) %>%
bold_labels()
table1| Characteristic | N = 4181 |
|---|---|
| Mets.Organ | |
| 8 (1.9%) | |
| Anal canal | 1 (0.2%) |
| Appendix, Adrenal gland | 1 (0.2%) |
| Appendix, Lymph Node | 1 (0.2%) |
| Bone | 1 (0.2%) |
| Cervix, Uterine body, Ovary | 1 (0.2%) |
| Colon, Appendix | 1 (0.2%) |
| Colon, Liver | 1 (0.2%) |
| Colon, Liver, Uterine body, Femoral nerve, Iliopsoas muscle | 1 (0.2%) |
| Colon, Lymph Node | 1 (0.2%) |
| Colon, Peritoneum | 2 (0.5%) |
| Colon, Rectum, Peritoneum | 1 (0.2%) |
| Gallbladder/Bile duct, Pancreas | 1 (0.2%) |
| Liver | 214 (51%) |
| Liver, Gallbladder/Bile duct | 38 (9.1%) |
| Liver, Lung | 1 (0.2%) |
| Liver, Lymph node | 2 (0.5%) |
| Liver, Peritoneum | 5 (1.2%) |
| Liver, Peritoneum, Diaphragm | 1 (0.2%) |
| Liver, Spleen | 1 (0.2%) |
| Lung | 73 (17%) |
| Lymph node | 16 (3.8%) |
| Lymph Node | 1 (0.2%) |
| Ovary | 1 (0.2%) |
| Peritoneum | 20 (4.8%) |
| Peritoneum, Ovary | 1 (0.2%) |
| Peritoneum, Pancreas, Spleen | 1 (0.2%) |
| Peritoneum, Uterine Body, Ovary | 1 (0.2%) |
| Rectum | 5 (1.2%) |
| Rectum, Cervix, Uterine body, Ovary, Vagina | 1 (0.2%) |
| Rectum, Liver, Appendix | 1 (0.2%) |
| Rectum, Lymph node | 2 (0.5%) |
| Small intestine | 1 (0.2%) |
| Small intestine, Colon, Liver, Pancreas, Peritoneum | 1 (0.2%) |
| Small intestine, Colon, Pancreas, Peritoneum | 1 (0.2%) |
| Small intestine, Colon, Rectum, Urinary tract | 2 (0.5%) |
| Small intestine, Colon, Urinary tract, Lymph node | 1 (0.2%) |
| Small intestine, Peritoneum, Urinary tract | 1 (0.2%) |
| Small intestine, Uterine body, Urinary tract | 1 (0.2%) |
| Spleen | 1 (0.2%) |
| Transverse colon, Descending colon | 1 (0.2%) |
| Urinary Tract | 1 (0.2%) |
| Urinary Tract, Lymph Node | 1 (0.2%) |
| 1 n (%) | |
fit1 <- as_flex_table(
table1,
include = everything(),
return_calls = FALSE,
strip_md_bold = TRUE)
fit1Characteristic | N = 4181 |
|---|---|
Mets.Organ | |
8 (1.9%) | |
Anal canal | 1 (0.2%) |
Appendix, Adrenal gland | 1 (0.2%) |
Appendix, Lymph Node | 1 (0.2%) |
Bone | 1 (0.2%) |
Cervix, Uterine body, Ovary | 1 (0.2%) |
Colon, Appendix | 1 (0.2%) |
Colon, Liver | 1 (0.2%) |
Colon, Liver, Uterine body, Femoral nerve, Iliopsoas muscle | 1 (0.2%) |
Colon, Lymph Node | 1 (0.2%) |
Colon, Peritoneum | 2 (0.5%) |
Colon, Rectum, Peritoneum | 1 (0.2%) |
Gallbladder/Bile duct, Pancreas | 1 (0.2%) |
Liver | 214 (51%) |
Liver, Gallbladder/Bile duct | 38 (9.1%) |
Liver, Lung | 1 (0.2%) |
Liver, Lymph node | 2 (0.5%) |
Liver, Peritoneum | 5 (1.2%) |
Liver, Peritoneum, Diaphragm | 1 (0.2%) |
Liver, Spleen | 1 (0.2%) |
Lung | 73 (17%) |
Lymph node | 16 (3.8%) |
Lymph Node | 1 (0.2%) |
Ovary | 1 (0.2%) |
Peritoneum | 20 (4.8%) |
Peritoneum, Ovary | 1 (0.2%) |
Peritoneum, Pancreas, Spleen | 1 (0.2%) |
Peritoneum, Uterine Body, Ovary | 1 (0.2%) |
Rectum | 5 (1.2%) |
Rectum, Cervix, Uterine body, Ovary, Vagina | 1 (0.2%) |
Rectum, Liver, Appendix | 1 (0.2%) |
Rectum, Lymph node | 2 (0.5%) |
Small intestine | 1 (0.2%) |
Small intestine, Colon, Liver, Pancreas, Peritoneum | 1 (0.2%) |
Small intestine, Colon, Pancreas, Peritoneum | 1 (0.2%) |
Small intestine, Colon, Rectum, Urinary tract | 2 (0.5%) |
Small intestine, Colon, Urinary tract, Lymph node | 1 (0.2%) |
Small intestine, Peritoneum, Urinary tract | 1 (0.2%) |
Small intestine, Uterine body, Urinary tract | 1 (0.2%) |
Spleen | 1 (0.2%) |
Transverse colon, Descending colon | 1 (0.2%) |
Urinary Tract | 1 (0.2%) |
Urinary Tract, Lymph Node | 1 (0.2%) |
1n (%) | |
save_as_docx(fit1, path= "~/Downloads/table1.docx")#ctDNA Detection Rates by Window and Stages
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data$ctDNA.Baseline <- factor(circ_data$ctDNA.Baseline, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Baseline %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Baseline == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
#ctDNA at Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
Stage = total_counts_by_stage$Group.1,
Total_Count = total_counts_by_stage$x,
Positive_Count = positive_counts_by_stage$x,
Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100 # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100 # Convert to percentage
overall_row <- data.frame(
Stage = "Overall",
Total_Count = overall_total_count,
Positive_Count = overall_positive_count,
Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)#DFS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 1773 233 NA NA NA
ctDNA.MRD=POSITIVE 336 263 5.34 4.83 6.7event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 625 224 0.851 0.00949 0.832 0.869
30 353 6 0.841 0.01025 0.820 0.860
36 131 2 0.835 0.01101 0.812 0.856
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 36 258 0.206 0.0236 0.161 0.254
30 21 3 0.185 0.0242 0.140 0.234
36 10 2 0.167 0.0250 0.121 0.219circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 2109, number of events= 496
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.48392 11.98819 0.09162 27.11 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 11.99 0.08342 10.02 14.35
Concordance= 0.738 (se = 0.01 )
Likelihood ratio test= 631.6 on 1 df, p=<2e-16
Wald test = 734.9 on 1 df, p=<2e-16
Score (logrank) test = 1164 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 1024)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 7.552801330068718912065829770600555032432112056605465295014722362661174896494570258855900533387391369137424336841591725250982187079459046905651457282443306379996927472184026988223096070980252409159977074130441915748770396333861485082783634801646179004987250992843624811754272870226285707769491524942224709792577e-162"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 11.99 (10.02-14.35); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 662.58, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
17.60071 32.35538
sample estimates:
odds ratio
23.75245 print(contingency_table)
No Recurrence Recurrence
Negative 1540 233
Positive 73 263table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 4.109122268663535020190194373428932515280107897593947698822434043794803736500514e-146"#DFS by ctDNA at the MRD Window - Stage I Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 226 7 NA NA NA
ctDNA.MRD=POSITIVE 2 2 15.3 0.526 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage I", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 67.0000 7.0000 0.9556 0.0176 0.9043 0.9797
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 1.00000 0.50000 0.35355 0.00598 0.91041 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 228, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.5700 35.5148 0.8291 4.306 1.66e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 35.51 0.02816 6.993 180.4
Concordance= 0.587 (se = 0.069 )
Likelihood ratio test= 9.72 on 1 df, p=0.002
Wald test = 18.54 on 1 df, p=2e-05
Score (logrank) test = 47.16 on 1 df, p=7e-12cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 35.51 (6.99-180.35); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 26.866, df = 1, p-value = 2.181e-07fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.001391
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
4.884572 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Recurrence Recurrence
Negative 219 7
Positive 0 2table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage I",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by ctDNA at the MRD Window - Stage II Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 584 30 NA NA NA
ctDNA.MRD=POSITIVE 45 30 7.75 5.45 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 234.0000 29.0000 0.9413 0.0108 0.9159 0.9592
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 29.0000 0.3250 0.0749 0.1864 0.4714 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 629, number of events= 60
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.1738 23.8977 0.2623 12.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 23.9 0.04184 14.29 39.96
Concordance= 0.745 (se = 0.031 )
Likelihood ratio test= 110.6 on 1 df, p=<2e-16
Wald test = 146.4 on 1 df, p=<2e-16
Score (logrank) test = 310.6 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 1.047913189863347989327367050096488019066134164836504729386419726654843614242491e-33"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 23.9 (14.29-39.96); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 176.25, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
16.95374 81.29786
sample estimates:
odds ratio
36.33032 print(contingency_table)
No Recurrence Recurrence
Negative 554 30
Positive 15 30table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage II",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 3.198206793772809443573423138766318999835327374042563666438913208024237106040795e-40"#DFS by ctDNA at the MRD Window - Stage III Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 683 82 NA NA NA
ctDNA.MRD=POSITIVE 162 117 9.48 7.16 11.7event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 242.0000 78.0000 0.8600 0.0152 0.8272 0.8870
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 22.000 115.000 0.259 0.037 0.190 0.334 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 845, number of events= 199
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3582 10.5722 0.1459 16.16 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 10.57 0.09459 7.942 14.07
Concordance= 0.752 (se = 0.016 )
Likelihood ratio test= 245 on 1 df, p=<2e-16
Wald test = 261.2 on 1 df, p=<2e-16
Score (logrank) test = 399.4 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 9.553609813712129515588467177845264510068272671813701043855574645317991879965595e-59"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 10.57 (7.94-14.07); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 260.38, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
12.35353 29.50193
sample estimates:
odds ratio
18.94366 print(contingency_table)
No Recurrence Recurrence
Negative 601 82
Positive 45 117table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage III",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.416838534651371444097784466385360624230007828478865587685460585181145547173816e-58"#DFS by ctDNA at the MRD Window - High Risk Stage II Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1481 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 475 24 NA NA NA
ctDNA.MRD=POSITIVE 42 28 7.56 4.99 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1481 observations deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 193.000 23.000 0.942 0.012 0.914 0.962
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 6.000 27.000 0.337 0.076 0.195 0.484 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 517, number of events= 52
(1481 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.2102 24.7836 0.2831 11.34 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 24.78 0.04035 14.23 43.16
Concordance= 0.764 (se = 0.033 )
Likelihood ratio test= 102.4 on 1 df, p=<2e-16
Wald test = 128.6 on 1 df, p=<2e-16
Score (logrank) test = 275.5 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 8.298468730124904390106245181286179365627816653344016735886626524065626176641230e-30"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 24.78 (14.23-43.16); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 155.19, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
16.43949 86.53043
sample estimates:
odds ratio
36.84362 print(contingency_table)
No Recurrence Recurrence
Negative 451 24
Positive 14 28table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - High Risk Stage II",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.269907566733626804565051781294123760003881097556484768496720886862426267774383e-35"#DFS by ctDNA at the MRD Window - High Risk Stage III Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageIII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1265 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 383 56 NA NA NA
ctDNA.MRD=POSITIVE 105 79 10.1 7.66 14event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | High Risk Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1265 observations deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 130.0000 53.0000 0.8322 0.0219 0.7842 0.8705
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 77.0000 0.2305 0.0443 0.1500 0.3214 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 488, number of events= 135
(1265 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2154 9.1654 0.1775 12.48 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.165 0.1091 6.472 12.98
Concordance= 0.74 (se = 0.019 )
Likelihood ratio test= 147.9 on 1 df, p=<2e-16
Wald test = 155.7 on 1 df, p=<2e-16
Score (logrank) test = 226.5 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 9.662617718197377484777606097839964514286084658708059450321796948040736949274378e-36"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.17 (6.47-12.98); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageIII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 148.3, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
10.18115 31.22155
sample estimates:
odds ratio
17.58359 print(contingency_table)
No Recurrence Recurrence
Negative 327 56
Positive 26 79table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - High Risk Stage III",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 4.075664440748080730981398793600671228492186717922852829612396737967570465527740e-34"#DFS by ctDNA at the MRD Window - Stage I-III Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 1493 119 NA NA NA
ctDNA.MRD=POSITIVE 209 149 8.9 7.16 10.7event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage I-III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 543 114 0.906 0.00864 0.888 0.922
30 299 3 0.900 0.00935 0.880 0.917
36 104 1 0.896 0.00987 0.875 0.914
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 29 145 0.273 0.0333 0.210 0.340
30 16 2 0.249 0.0347 0.184 0.319
36 7 2 0.218 0.0367 0.151 0.293circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 1702, number of events= 268
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.7704 15.9658 0.1243 22.29 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 15.97 0.06263 12.51 20.37
Concordance= 0.758 (se = 0.014 )
Likelihood ratio test= 425.1 on 1 df, p=<2e-16
Wald test = 496.7 on 1 df, p=<2e-16
Score (logrank) test = 887.8 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 4.89310881335822997122369330866796366143543744261804739847188427503089230135526010889580154112538230913655461810983258781049832056753645199388487490336267101e-110"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 15.97 (12.51-20.37); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 549.33, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
19.84311 41.51239
sample estimates:
odds ratio
28.54032 print(contingency_table)
No Recurrence Recurrence
NEGATIVE 1374 119
POSITIVE 60 149table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage I-III",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.761535954254352737571030791916104110296714135375184233087868467861543082396972e-121"#DFS by ctDNA at the MRD Window - Stage IV Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 280 114 NA 26.91 NA
ctDNA.MRD=POSITIVE 127 114 2.83 2.17 4.21event_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage IV", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 82 110 0.575 0.0319 0.510 0.634
30 54 3 0.551 0.0334 0.484 0.614
36 27 1 0.538 0.0351 0.467 0.604
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 7 113 0.0924 0.0274 0.0479 0.155
30 5 1 0.0770 0.0268 0.0353 0.140
36 3 0 0.0770 0.0268 0.0353 0.140circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 407, number of events= 228
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.7624 5.8266 0.1384 12.73 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 5.827 0.1716 4.442 7.642
Concordance= 0.695 (se = 0.013 )
Likelihood ratio test= 148.1 on 1 df, p=<2e-16
Wald test = 162.2 on 1 df, p=<2e-16
Score (logrank) test = 200.2 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 3.819545962002338094606875172021182780623116378373650079530181894187304050138118e-37"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.83 (4.44-7.64); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 83.338, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
6.724737 25.779487
sample estimates:
odds ratio
12.69031 print(contingency_table)
No Recurrence Recurrence
Negative 166 114
Positive 13 114table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage IV",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 6.915297012856293933806903135607333574028524365862464736223746974985715496586636e-20"#OS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 1773 36 NA NA NA
ctDNA.MRD=POSITIVE 336 52 43.4 NA NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 825 18 0.985 0.00349 0.977 0.991
30 497 13 0.968 0.00593 0.954 0.978
36 185 4 0.960 0.00722 0.943 0.972
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 119 37 0.837 0.0258 0.778 0.881
30 73 9 0.769 0.0323 0.698 0.825
36 24 4 0.718 0.0388 0.634 0.786circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 2109, number of events= 88
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.271 9.685 0.217 10.46 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.685 0.1033 6.33 14.82
Concordance= 0.754 (se = 0.027 )
Likelihood ratio test= 103.2 on 1 df, p=<2e-16
Wald test = 109.5 on 1 df, p=<2e-16
Score (logrank) test = 165.2 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 1.253776275433807207102491111491442110056823754364236931021911952166414111268953e-25"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.68 (6.33-14.82); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 124.38, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
5.545286 14.164077
sample estimates:
odds ratio
8.818776 print(contingency_table)
Alive Deceased
Negative 1737 36
Positive 284 52table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 6.972872016053109372211544772713944632093425193788097848484148685972027167695626e-29"#OS by ctDNA at the MRD Window - Stage I-III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 1493 23 NA NA NA
ctDNA.MRD=POSITIVE 209 25 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | Stage I-III", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 670 14 0.985 0.00409 0.974 0.991
30 365 9 0.968 0.00701 0.951 0.979
36 105 0 0.968 0.00701 0.951 0.979
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 70 20 0.847 0.0335 0.767 0.901
30 42 0 0.847 0.0335 0.767 0.901
36 10 5 0.693 0.0803 0.506 0.821circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 1702, number of events= 48
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.2661 9.6416 0.2891 7.838 4.57e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 9.642 0.1037 5.471 16.99
Concordance= 0.732 (se = 0.038 )
Likelihood ratio test= 52.89 on 1 df, p=4e-13
Wald test = 61.44 on 1 df, p=5e-15
Score (logrank) test = 92.58 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 4.567662200511931517917896541543163204147688509787224475024937112759850155321226e-15"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 9.64 (5.47-16.99); p = 0"circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 68.895, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 1.07e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
4.614397 16.339262
sample estimates:
odds ratio
8.662461 print(contingency_table)
Alive Deceased
NEGATIVE 1470 23
POSITIVE 184 25table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window - Stage I-III",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.038238957954624065192297740448887943524512950197596916623865581641439348459244e-16"#OS by ctDNA at the MRD Window - Stage IV
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 280 13 NA NA NA
ctDNA.MRD=POSITIVE 125 27 41.8 NA NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | Stage IV", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 138 8 0.959 0.0147 0.918 0.980
30 83 3 0.934 0.0205 0.879 0.964
36 34 1 0.922 0.0232 0.862 0.957
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 43 21 0.750 0.0499 0.636 0.833
30 24 5 0.655 0.0591 0.526 0.757
36 8 0 0.655 0.0591 0.526 0.757circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 405, number of events= 40
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 1.8308 6.2388 0.3384 5.411 6.28e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 6.239 0.1603 3.214 12.11
Concordance= 0.73 (se = 0.037 )
Likelihood ratio test= 31.78 on 1 df, p=2e-08
Wald test = 29.27 on 1 df, p=6e-08
Score (logrank) test = 38.28 on 1 df, p=6e-10cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.24 (3.21-12.11); p = 0"circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 26.045, df = 1, p-value = 3.336e-07fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 6.728e-07
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.681627 12.393229
sample estimates:
odds ratio
5.630069 print(contingency_table)
Alive Deceased
NEGATIVE 267 13
POSITIVE 98 27table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Multivariate cox regression at MRD Window for DFS - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD + Gender + Age.Group +
PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data = circ_data)
n= 1824, number of events= 337
(286 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPositive 2.4918 12.0825 0.1194 20.865 < 2e-16 ***
GenderMale 0.1577 1.1708 0.1124 1.403 0.160477
Age.Group>70 -0.1532 0.8580 0.1143 -1.340 0.180165
PrimSiteRight-sided colon 0.2046 1.2270 0.1183 1.729 0.083802 .
ECOG1 0.1920 1.2117 0.1854 1.036 0.300192
pTT3-T4 0.5113 1.6674 0.2373 2.154 0.031218 *
pNN1-N2 0.4429 1.5572 0.1357 3.264 0.001098 **
MSIMSI-High -1.5477 0.2127 0.4069 -3.803 0.000143 ***
BRAF.V600EV600E 0.7070 2.0280 0.2717 2.602 0.009261 **
RASMutant 0.3566 1.4284 0.1168 3.053 0.002267 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPositive 12.0825 0.08276 9.56104 15.2689
GenderMale 1.1708 0.85409 0.93938 1.4593
Age.Group>70 0.8580 1.16552 0.68581 1.0734
PrimSiteRight-sided colon 1.2270 0.81499 0.97305 1.5473
ECOG1 1.2117 0.82527 0.84260 1.7426
pTT3-T4 1.6674 0.59973 1.04720 2.6550
pNN1-N2 1.5572 0.64220 1.19356 2.0315
MSIMSI-High 0.2127 4.70083 0.09582 0.4723
BRAF.V600EV600E 2.0280 0.49311 1.19067 3.4541
RASMutant 1.4284 0.70008 1.13614 1.7958
Concordance= 0.836 (se = 0.01 )
Likelihood ratio test= 587.1 on 10 df, p=<2e-16
Wald test = 609.6 on 10 df, p=<2e-16
Score (logrank) test = 1076 on 10 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPositive", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 1.10491338314482535047684564465763026763567054940930072955327678379182870575438158895286003945965111479324501573041745064343378898409260175641308997605671827e-96"#Multivariate cox regression at MRD Window for OS - All stages Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD + Gender + Age.Group +
PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data = circ_data)
n= 1824, number of events= 60
(286 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPositive 2.2892 9.8668 0.2894 7.911 2.55e-15 ***
GenderMale 0.1567 1.1696 0.2685 0.584 0.5595
Age.Group>70 0.5072 1.6607 0.2787 1.820 0.0688 .
PrimSiteRight-sided colon 0.2426 1.2746 0.2810 0.863 0.3879
ECOG1 0.3982 1.4891 0.3615 1.101 0.2707
pTT3-T4 -0.0915 0.9126 0.4818 -0.190 0.8494
pNN1-N2 0.7855 2.1936 0.3428 2.291 0.0219 *
MSIMSI-High -1.6650 0.1892 0.7875 -2.114 0.0345 *
BRAF.V600EV600E 2.0072 7.4428 0.4855 4.134 3.56e-05 ***
RASMutant 0.6564 1.9278 0.3007 2.183 0.0290 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPositive 9.8669 0.1013 5.59590 17.3975
GenderMale 1.1696 0.8550 0.69108 1.9795
Age.Group>70 1.6607 0.6021 0.96175 2.8677
PrimSiteRight-sided colon 1.2746 0.7846 0.73478 2.2111
ECOG1 1.4891 0.6716 0.73322 3.0241
pTT3-T4 0.9126 1.0958 0.35492 2.3463
pNN1-N2 2.1936 0.4559 1.12028 4.2953
MSIMSI-High 0.1892 5.2856 0.04041 0.8857
BRAF.V600EV600E 7.4428 0.1344 2.87398 19.2748
RASMutant 1.9278 0.5187 1.06931 3.4757
Concordance= 0.832 (se = 0.033 )
Likelihood ratio test= 111.4 on 10 df, p=<2e-16
Wald test = 108.2 on 10 df, p=<2e-16
Score (logrank) test = 161.4 on 10 df, p=<2e-16cox_fit_summary <- summary(cox_fit)#DFS by ACT treatment in MRD negative - High Risk Stage II/III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
15 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ACT=FALSE 586 50 NA NA NA
ACT=TRUE 571 55 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
15 observations deleted due to missingness
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 215.000 49.000 0.899 0.014 0.868 0.923
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 216.0000 51.0000 0.8911 0.0148 0.8581 0.9168 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 1157, number of events= 105
(15 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE -0.1149 0.8915 0.1954 -0.588 0.557
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 0.8915 1.122 0.6078 1.307
Concordance= 0.508 (se = 0.025 )
Likelihood ratio test= 0.35 on 1 df, p=0.6
Wald test = 0.35 on 1 df, p=0.6
Score (logrank) test = 0.35 on 1 df, p=0.6cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.89 (0.61-1.31); p = 0.557"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.30112, df = 1, p-value = 0.5832fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.5401
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5730366 1.3343425
sample estimates:
odds ratio
0.8752713 print(contingency_table)
No Recurrence Recurrence
TRUE 516 55
FALSE 536 50table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, ECOG and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 1157, number of events= 105
(15 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.3623 1.4367 0.2145 1.689 0.0911 .
GenderMale 0.1477 1.1591 0.1960 0.753 0.4512
Age.Group≥70 -0.3075 0.7353 0.2067 -1.487 0.1369
StageIII 1.0528 2.8656 0.2528 4.164 3.13e-05 ***
ECOG1 0.2435 1.2756 0.3168 0.769 0.4422
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.4367 0.6961 0.9436 2.187
GenderMale 1.1591 0.8627 0.7894 1.702
Age.Group≥70 0.7353 1.3600 0.4903 1.103
StageIII 2.8656 0.3490 1.7458 4.704
ECOG1 1.2756 0.7839 0.6856 2.373
Concordance= 0.629 (se = 0.026 )
Likelihood ratio test= 23.38 on 5 df, p=3e-04
Wald test = 21.22 on 5 df, p=7e-04
Score (logrank) test = 22.35 on 5 df, p=4e-04#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 1157, number of events= 105
(15 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.3623 0.6961 0.2145 -1.689 0.0911 .
GenderMale 0.1477 1.1591 0.1960 0.753 0.4512
Age.Group≥70 -0.3075 0.7353 0.2067 -1.487 0.1369
StageIII 1.0528 2.8656 0.2528 4.164 3.13e-05 ***
ECOG1 0.2435 1.2756 0.3168 0.769 0.4422
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.6961 1.4367 0.4572 1.060
GenderMale 1.1591 0.8627 0.7894 1.702
Age.Group≥70 0.7353 1.3600 0.4903 1.103
StageIII 2.8656 0.3490 1.7458 4.704
ECOG1 1.2756 0.7839 0.6856 2.373
Concordance= 0.629 (se = 0.026 )
Likelihood ratio test= 23.38 on 5 df, p=3e-04
Wald test = 21.22 on 5 df, p=7e-04
Score (logrank) test = 22.35 on 5 df, p=4e-04#DFS by ACT treatment in MRD positive - High Risk Stage II/III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ACT=FALSE 47 45 3.55 3.16 3.95
ACT=TRUE 145 88 12.06 9.30 18.57event_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
1 observation deleted due to missingness
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 44.00000 0.02837 0.02746 0.00232 0.12350
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 25.0000 87.0000 0.3583 0.0435 0.2741 0.4432 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 192, number of events= 133
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.4203 4.1382 0.1901 7.472 7.91e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.138 0.2417 2.851 6.006
Concordance= 0.634 (se = 0.019 )
Likelihood ratio test= 46.68 on 1 df, p=8e-12
Wald test = 55.83 on 1 df, p=8e-14
Score (logrank) test = 64.71 on 1 df, p=9e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.14 (2.85-6.01); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 18.877, df = 1, p-value = 1.394e-05fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 1.043e-06
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
3.527208 127.775372
sample estimates:
odds ratio
14.42898 print(contingency_table)
No Recurrence Recurrence
TRUE 57 88
FALSE 2 45table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 192, number of events= 133
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.46226 4.31571 0.20651 7.081 1.43e-12 ***
GenderMale -0.06402 0.93799 0.18183 -0.352 0.725
Age.Group≥70 0.03736 1.03807 0.18637 0.200 0.841
StageIII 0.31989 1.37697 0.23571 1.357 0.175
ECOG1 0.05652 1.05814 0.28089 0.201 0.841
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.316 0.2317 2.8792 6.469
GenderMale 0.938 1.0661 0.6568 1.340
Age.Group≥70 1.038 0.9633 0.7204 1.496
StageIII 1.377 0.7262 0.8675 2.186
ECOG1 1.058 0.9451 0.6102 1.835
Concordance= 0.644 (se = 0.026 )
Likelihood ratio test= 49.19 on 5 df, p=2e-09
Wald test = 58.77 on 5 df, p=2e-11
Score (logrank) test = 67.68 on 5 df, p=3e-13#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + Stage +
ECOG, data = circ_data)
n= 192, number of events= 133
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -1.46226 0.23171 0.20651 -7.081 1.43e-12 ***
GenderMale -0.06402 0.93799 0.18183 -0.352 0.725
Age.Group≥70 0.03736 1.03807 0.18637 0.200 0.841
StageIII 0.31989 1.37697 0.23571 1.357 0.175
ECOG1 0.05652 1.05814 0.28089 0.201 0.841
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.2317 4.3157 0.1546 0.3473
GenderMale 0.9380 1.0661 0.6568 1.3396
Age.Group≥70 1.0381 0.9633 0.7204 1.4958
StageIII 1.3770 0.7262 0.8675 2.1855
ECOG1 1.0581 0.9451 0.6102 1.8350
Concordance= 0.644 (se = 0.026 )
Likelihood ratio test= 49.19 on 5 df, p=2e-09
Wald test = 58.77 on 5 df, p=2e-11
Score (logrank) test = 67.68 on 5 df, p=3e-13#DFS by ACT treatment in MRD negative - High Risk Stage II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
1588 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ACT=FALSE 373 21 NA NA NA
ACT=TRUE 102 3 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
1588 observations deleted due to missingness
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 152.000 20.000 0.937 0.014 0.903 0.959
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 38.0000 3.0000 0.9634 0.0211 0.8890 0.9883 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 475, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.6344 1.8860 0.6173 1.028 0.304
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.886 0.5302 0.5625 6.323
Concordance= 0.544 (se = 0.035 )
Likelihood ratio test= 1.23 on 1 df, p=0.3
Wald test = 1.06 on 1 df, p=0.3
Score (logrank) test = 1.09 on 1 df, p=0.3cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.89 (0.56-6.32); p = 0.304"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.71169, df = 1, p-value = 0.3989fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.4423
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5696505 10.5052330
sample estimates:
odds ratio
1.966313 print(contingency_table)
No Recurrence Recurrence
TRUE 99 3
FALSE 352 21table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 475, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.7519 2.1211 0.6266 1.200 0.2301
GenderMale -0.1514 0.8595 0.4160 -0.364 0.7159
Age.Group≥70 -0.8105 0.4446 0.4420 -1.834 0.0667 .
ECOG1 0.5506 1.7343 0.5794 0.950 0.3419
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 2.1211 0.4715 0.6212 7.243
GenderMale 0.8595 1.1634 0.3803 1.943
Age.Group≥70 0.4446 2.2490 0.1870 1.057
ECOG1 1.7343 0.5766 0.5571 5.399
Concordance= 0.629 (se = 0.06 )
Likelihood ratio test= 4.98 on 4 df, p=0.3
Wald test = 4.66 on 4 df, p=0.3
Score (logrank) test = 4.79 on 4 df, p=0.3#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 475, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.7519 0.4715 0.6266 -1.200 0.2301
GenderMale -0.1514 0.8595 0.4160 -0.364 0.7159
Age.Group≥70 -0.8105 0.4446 0.4420 -1.834 0.0667 .
ECOG1 0.5506 1.7343 0.5794 0.950 0.3419
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.4715 2.1211 0.1381 1.610
GenderMale 0.8595 1.1634 0.3803 1.943
Age.Group≥70 0.4446 2.2490 0.1870 1.057
ECOG1 1.7343 0.5766 0.5571 5.399
Concordance= 0.629 (se = 0.06 )
Likelihood ratio test= 4.98 on 4 df, p=0.3
Wald test = 4.66 on 4 df, p=0.3
Score (logrank) test = 4.79 on 4 df, p=0.3#DFS by ACT treatment in MRD positive - High Risk Stage II
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
1588 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ACT=FALSE 15 14 3.52 3.39 NA
ACT=TRUE 23 10 NA 9.30 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
1588 observations deleted due to missingness
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.00000 1.00000 13.00000 0.10000 0.08756 0.00781 0.33528
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 5.000 10.000 0.537 0.110 0.305 0.722 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 38, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.6902 5.4206 0.4305 3.926 8.64e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 5.421 0.1845 2.331 12.6
Concordance= 0.709 (se = 0.039 )
Likelihood ratio test= 15.26 on 1 df, p=9e-05
Wald test = 15.41 on 1 df, p=9e-05
Score (logrank) test = 18.65 on 1 df, p=2e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.42 (2.33-12.6); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.6738, df = 1, p-value = 0.005603fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.002121
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.954984 823.248016
sample estimates:
odds ratio
16.90572 print(contingency_table)
No Recurrence Recurrence
TRUE 13 10
FALSE 1 14table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 38, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 2.0475 7.7483 0.5070 4.039 5.38e-05 ***
GenderMale -0.4489 0.6383 0.4772 -0.941 0.3469
Age.Group≥70 0.2109 1.2348 0.4996 0.422 0.6729
ECOG1 1.4282 4.1714 0.5908 2.417 0.0156 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 7.7483 0.1291 2.8686 20.928
GenderMale 0.6383 1.5666 0.2505 1.626
Age.Group≥70 1.2348 0.8098 0.4638 3.288
ECOG1 4.1714 0.2397 1.3103 13.280
Concordance= 0.759 (se = 0.052 )
Likelihood ratio test= 22.42 on 4 df, p=2e-04
Wald test = 19.09 on 4 df, p=8e-04
Score (logrank) test = 25.15 on 4 df, p=5e-05#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 38, number of events= 24
(1588 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -2.0475 0.1291 0.5070 -4.039 5.38e-05 ***
GenderMale -0.4489 0.6383 0.4772 -0.941 0.3469
Age.Group≥70 0.2109 1.2348 0.4996 0.422 0.6729
ECOG1 1.4282 4.1714 0.5908 2.417 0.0156 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.1291 7.7483 0.04778 0.3486
GenderMale 0.6383 1.5666 0.25054 1.6264
Age.Group≥70 1.2348 0.8098 0.46377 3.2878
ECOG1 4.1714 0.2397 1.31029 13.2798
Concordance= 0.759 (se = 0.052 )
Likelihood ratio test= 22.42 on 4 df, p=2e-04
Wald test = 19.09 on 4 df, p=8e-04
Score (logrank) test = 25.15 on 4 df, p=5e-05#DFS by ACT treatment in MRD negative - Stage II T3N0
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 400 17 NA NA NA
ACT=TRUE 76 1 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | T3N0", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 166.0000 16.0000 0.9516 0.0121 0.9212 0.9704
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 31.0000 1.0000 0.9811 0.0187 0.8735 0.9973 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 476, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.195 3.304 1.029 1.161 0.246
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 3.304 0.3027 0.4396 24.83
Concordance= 0.559 (se = 0.023 )
Likelihood ratio test= 1.94 on 1 df, p=0.2
Wald test = 1.35 on 1 df, p=0.2
Score (logrank) test = 1.52 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.3 (0.44-24.83); p = 0.246"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.81236, df = 1, p-value = 0.3674fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.3307
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.5053922 140.8837116
sample estimates:
odds ratio
3.323355 print(contingency_table)
No Recurrence Recurrence
TRUE 75 1
FALSE 383 17table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 476, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.3971 4.0433 1.0319 1.354 0.1758
GenderMale 0.1738 1.1898 0.4719 0.368 0.7127
Age.Group≥70 -1.3071 0.2706 0.5576 -2.344 0.0191 *
ECOG1 0.4088 1.5051 0.7931 0.516 0.6062
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.0433 0.2473 0.53501 30.5566
GenderMale 1.1898 0.8405 0.47186 3.0000
Age.Group≥70 0.2706 3.6955 0.09072 0.8072
ECOG1 1.5051 0.6644 0.31805 7.1221
Concordance= 0.688 (se = 0.041 )
Likelihood ratio test= 8.51 on 4 df, p=0.07
Wald test = 7.13 on 4 df, p=0.1
Score (logrank) test = 7.92 on 4 df, p=0.09#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 476, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -1.3971 0.2473 1.0319 -1.354 0.1758
GenderMale 0.1738 1.1898 0.4719 0.368 0.7127
Age.Group≥70 -1.3071 0.2706 0.5576 -2.344 0.0191 *
ECOG1 0.4088 1.5051 0.7931 0.516 0.6062
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.2473 4.0433 0.03273 1.8691
GenderMale 1.1898 0.8405 0.47186 3.0000
Age.Group≥70 0.2706 3.6955 0.09072 0.8072
ECOG1 1.5051 0.6644 0.31805 7.1221
Concordance= 0.688 (se = 0.041 )
Likelihood ratio test= 8.51 on 4 df, p=0.07
Wald test = 7.13 on 4 df, p=0.1
Score (logrank) test = 7.92 on 4 df, p=0.09#DFS by ACT treatment in MRD negative - Stage II T4N0
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 64 9 NA NA NA
ACT=TRUE 29 2 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | T4N0", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 19.0000 9.0000 0.8478 0.0471 0.7267 0.9181
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 2.0000 0.9205 0.0544 0.7154 0.9797 circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 93, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.6570 1.9290 0.7824 0.84 0.401
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.929 0.5184 0.4162 8.94
Concordance= 0.561 (se = 0.06 )
Likelihood ratio test= 0.8 on 1 df, p=0.4
Wald test = 0.71 on 1 df, p=0.4
Score (logrank) test = 0.73 on 1 df, p=0.4cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.93 (0.42-8.94); p = 0.401"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.41565, df = 1, p-value = 0.5191fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.4927
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.4118588 22.2431792
sample estimates:
odds ratio
2.192682 print(contingency_table)
No Recurrence Recurrence
TRUE 27 2
FALSE 55 9table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 93, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.6626 1.9399 0.8154 0.813 0.416
GenderMale -0.1393 0.8700 0.6220 -0.224 0.823
Age.Group≥70 -0.3472 0.7066 0.6563 -0.529 0.597
ECOG1 0.3212 1.3788 0.8365 0.384 0.701
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.9399 0.5155 0.3924 9.591
GenderMale 0.8700 1.1494 0.2571 2.944
Age.Group≥70 0.7066 1.4152 0.1952 2.557
ECOG1 1.3788 0.7253 0.2676 7.104
Concordance= 0.588 (se = 0.075 )
Likelihood ratio test= 1.17 on 4 df, p=0.9
Wald test = 1.08 on 4 df, p=0.9
Score (logrank) test = 1.11 on 4 df, p=0.9#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 93, number of events= 11
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.6626 0.5155 0.8154 -0.813 0.416
GenderMale -0.1393 0.8700 0.6220 -0.224 0.823
Age.Group≥70 -0.3472 0.7066 0.6563 -0.529 0.597
ECOG1 0.3212 1.3788 0.8365 0.384 0.701
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.5155 1.9399 0.1043 2.549
GenderMale 0.8700 1.1494 0.2571 2.944
Age.Group≥70 0.7066 1.4152 0.1952 2.557
ECOG1 1.3788 0.7253 0.2676 7.104
Concordance= 0.588 (se = 0.075 )
Likelihood ratio test= 1.17 on 4 df, p=0.9
Wald test = 1.08 on 4 df, p=0.9
Score (logrank) test = 1.11 on 4 df, p=0.9#DFS by ACT treatment in MRD negative - Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 213 29 NA NA NA
ACT=TRUE 469 52 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 115 27 0.848 0.0274 0.785 0.894
24 63 2 0.829 0.0300 0.760 0.879
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 293 42 0.898 0.0150 0.864 0.924
24 178 6 0.876 0.0173 0.837 0.906circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 682, number of events= 81
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.2863 1.3315 0.2319 1.235 0.217
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.332 0.751 0.8452 2.098
Concordance= 0.537 (se = 0.028 )
Likelihood ratio test= 1.48 on 1 df, p=0.2
Wald test = 1.52 on 1 df, p=0.2
Score (logrank) test = 1.53 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.33 (0.85-2.1); p = 0.217"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.66893, df = 1, p-value = 0.4134fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.3718
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.7476624 2.1022451
sample estimates:
odds ratio
1.263412 print(contingency_table)
No Recurrence Recurrence
TRUE 417 52
FALSE 184 29table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 682, number of events= 81
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.3004 1.3505 0.2340 1.284 0.199
GenderMale 0.2382 1.2690 0.2244 1.062 0.288
Age.Group≥70 -0.1732 0.8410 0.2327 -0.744 0.457
ECOG1 0.1347 1.1442 0.3823 0.352 0.725
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.350 0.7405 0.8537 2.136
GenderMale 1.269 0.7880 0.8175 1.970
Age.Group≥70 0.841 1.1891 0.5329 1.327
ECOG1 1.144 0.8740 0.5409 2.420
Concordance= 0.553 (se = 0.033 )
Likelihood ratio test= 3.24 on 4 df, p=0.5
Wald test = 3.28 on 4 df, p=0.5
Score (logrank) test = 3.29 on 4 df, p=0.5#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 682, number of events= 81
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.3004 0.7405 0.2340 -1.284 0.199
GenderMale 0.2382 1.2690 0.2244 1.062 0.288
Age.Group≥70 -0.1732 0.8410 0.2327 -0.744 0.457
ECOG1 0.1347 1.1442 0.3823 0.352 0.725
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.7405 1.350 0.4681 1.171
GenderMale 1.2690 0.788 0.8175 1.970
Age.Group≥70 0.8410 1.189 0.5329 1.327
ECOG1 1.1442 0.874 0.5409 2.420
Concordance= 0.553 (se = 0.033 )
Likelihood ratio test= 3.24 on 4 df, p=0.5
Wald test = 3.28 on 4 df, p=0.5
Score (logrank) test = 3.29 on 4 df, p=0.5#DFS by ACT treatment in MRD positive - Stage III
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 32 31 3.58 2.57 4.01
ACT=TRUE 122 78 11.27 9.10 16.07event_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18.00000 1.00000 30.00000 0.03906 0.03744 0.00306 0.16257
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
18 39 72 0.393 0.0455 0.304 0.481
24 20 5 0.330 0.0464 0.241 0.421circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 154, number of events= 109
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.4135 4.1105 0.2203 6.417 1.39e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.11 0.2433 2.669 6.33
Concordance= 0.619 (se = 0.021 )
Likelihood ratio test= 33.3 on 1 df, p=8e-09
Wald test = 41.18 on 1 df, p=1e-10
Score (logrank) test = 47.85 on 1 df, p=5e-12cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 4.11 (2.67-6.33); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 11.755, df = 1, p-value = 0.0006068fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0001195
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.685315 726.194688
sample estimates:
odds ratio
17.29321 print(contingency_table)
No Recurrence Recurrence
TRUE 44 78
FALSE 1 31table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 154, number of events= 109
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.48178 4.40077 0.24173 6.130 8.79e-10 ***
GenderMale 0.02384 1.02413 0.19953 0.119 0.905
Age.Group≥70 -0.01673 0.98341 0.20368 -0.082 0.935
ECOG1 -0.20242 0.81675 0.33241 -0.609 0.543
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 4.4008 0.2272 2.7401 7.068
GenderMale 1.0241 0.9764 0.6926 1.514
Age.Group≥70 0.9834 1.0169 0.6597 1.466
ECOG1 0.8168 1.2244 0.4257 1.567
Concordance= 0.63 (se = 0.027 )
Likelihood ratio test= 33.75 on 4 df, p=8e-07
Wald test = 41.58 on 4 df, p=2e-08
Score (logrank) test = 48.35 on 4 df, p=8e-10#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 154, number of events= 109
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -1.48178 0.22723 0.24173 -6.130 8.79e-10 ***
GenderMale 0.02384 1.02413 0.19953 0.119 0.905
Age.Group≥70 -0.01673 0.98341 0.20368 -0.082 0.935
ECOG1 -0.20242 0.81675 0.33241 -0.609 0.543
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.2272 4.4008 0.1415 0.3649
GenderMale 1.0241 0.9764 0.6926 1.5142
Age.Group≥70 0.9834 1.0169 0.6597 1.4659
ECOG1 0.8168 1.2244 0.4257 1.5669
Concordance= 0.63 (se = 0.027 )
Likelihood ratio test= 33.75 on 4 df, p=8e-07
Wald test = 41.58 on 4 df, p=2e-08
Score (logrank) test = 48.35 on 4 df, p=8e-10#DFS by ACT treatment in MRD negative - Stage IV NAC-treated
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 113 53 27.9 15.3 NA
ACT=TRUE 30 11 NA 20.1 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage IV NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 103 10 0.912 0.0267 0.842 0.951
6 83 20 0.735 0.0415 0.643 0.806
18 48 20 0.535 0.0490 0.435 0.625
24 29 2 0.504 0.0509 0.400 0.598
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 30 0 1.000 0.0000 1.000 1.000
6 25 5 0.833 0.0680 0.645 0.927
18 16 4 0.689 0.0871 0.484 0.825
24 11 2 0.589 0.0992 0.373 0.753circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 143, number of events= 64
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.3749 1.4549 0.3314 1.131 0.258
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.455 0.6873 0.7598 2.786
Concordance= 0.535 (se = 0.024 )
Likelihood ratio test= 1.39 on 1 df, p=0.2
Wald test = 1.28 on 1 df, p=0.3
Score (logrank) test = 1.29 on 1 df, p=0.3cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.45 (0.76-2.79); p = 0.258"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.63325, df = 1, p-value = 0.4262fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.4094
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6212468 3.8850136
sample estimates:
odds ratio
1.521304 print(contingency_table)
No Recurrence Recurrence
TRUE 19 11
FALSE 60 53table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 143, number of events= 64
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.3908 1.4781 0.3332 1.173 0.241
GenderMale 0.3629 1.4375 0.2635 1.377 0.168
Age.Group≥70 -0.3175 0.7279 0.2697 -1.178 0.239
ECOG1 -0.5519 0.5759 0.7251 -0.761 0.447
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.4781 0.6765 0.7694 2.840
GenderMale 1.4375 0.6956 0.8577 2.409
Age.Group≥70 0.7279 1.3737 0.4291 1.235
ECOG1 0.5759 1.7365 0.1390 2.385
Concordance= 0.574 (se = 0.036 )
Likelihood ratio test= 5.95 on 4 df, p=0.2
Wald test = 5.5 on 4 df, p=0.2
Score (logrank) test = 5.6 on 4 df, p=0.2#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 143, number of events= 64
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.3908 0.6765 0.3332 -1.173 0.241
GenderMale 0.3629 1.4375 0.2635 1.377 0.168
Age.Group≥70 -0.3175 0.7279 0.2697 -1.178 0.239
ECOG1 -0.5519 0.5759 0.7251 -0.761 0.447
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.6765 1.4781 0.3521 1.300
GenderMale 1.4375 0.6956 0.8577 2.409
Age.Group≥70 0.7279 1.3737 0.4291 1.235
ECOG1 0.5759 1.7365 0.1390 2.385
Concordance= 0.574 (se = 0.036 )
Likelihood ratio test= 5.95 on 4 df, p=0.2
Wald test = 5.5 on 4 df, p=0.2
Score (logrank) test = 5.6 on 4 df, p=0.2#DFS by ACT treatment in MRD Negative - Stage IV no NAC-treated
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 81 30 NA 33.1 NA
ACT=TRUE 50 14 NA NA NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage IV No NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 75 6 0.926 0.0291 0.843 0.966
6 73 2 0.901 0.0331 0.812 0.949
18 44 19 0.655 0.0541 0.538 0.750
24 25 1 0.636 0.0559 0.516 0.734
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 49 1 0.980 0.0198 0.866 0.997
6 48 1 0.960 0.0277 0.849 0.990
18 29 9 0.765 0.0623 0.615 0.863
24 16 3 0.658 0.0790 0.479 0.787circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 131, number of events= 44
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.3877 1.4736 0.3240 1.197 0.231
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.474 0.6786 0.7809 2.781
Concordance= 0.56 (se = 0.035 )
Likelihood ratio test= 1.49 on 1 df, p=0.2
Wald test = 1.43 on 1 df, p=0.2
Score (logrank) test = 1.45 on 1 df, p=0.2cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.47 (0.78-2.78); p = 0.231"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.76302, df = 1, p-value = 0.3824fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.343
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6637884 3.5367388
sample estimates:
odds ratio
1.507881 print(contingency_table)
No Recurrence Recurrence
TRUE 36 14
FALSE 51 30table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 131, number of events= 44
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.2636 1.3016 0.3355 0.786 0.4320
GenderMale 0.3385 1.4029 0.3255 1.040 0.2983
Age.Group≥70 0.7056 2.0251 0.3167 2.228 0.0259 *
ECOG1 -1.5549 0.2112 1.0202 -1.524 0.1275
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 1.3016 0.7683 0.6744 2.512
GenderMale 1.4029 0.7128 0.7412 2.655
Age.Group≥70 2.0251 0.4938 1.0886 3.767
ECOG1 0.2112 4.7344 0.0286 1.560
Concordance= 0.64 (se = 0.042 )
Likelihood ratio test= 10.99 on 4 df, p=0.03
Wald test = 10.06 on 4 df, p=0.04
Score (logrank) test = 10.67 on 4 df, p=0.03#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 131, number of events= 44
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.2636 0.7683 0.3355 -0.786 0.4320
GenderMale 0.3385 1.4029 0.3255 1.040 0.2983
Age.Group≥70 0.7056 2.0251 0.3167 2.228 0.0259 *
ECOG1 -1.5549 0.2112 1.0202 -1.524 0.1275
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.7683 1.3016 0.3981 1.483
GenderMale 1.4029 0.7128 0.7412 2.655
Age.Group≥70 2.0251 0.4938 1.0886 3.767
ECOG1 0.2112 4.7344 0.0286 1.560
Concordance= 0.64 (se = 0.042 )
Likelihood ratio test= 10.99 on 4 df, p=0.03
Wald test = 10.06 on 4 df, p=0.04
Score (logrank) test = 10.67 on 4 df, p=0.03#DFS by ACT treatment in MRD positive - Stage IV NAC-treated
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 32 32 1.46 0.86 2.44
ACT=TRUE 14 13 3.78 3.13 12.59event_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage IV NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 7 25 0.2188 0.0731 0.09649 0.372
6 2 5 0.0625 0.0428 0.01112 0.181
18 1 1 0.0312 0.0308 0.00237 0.137
24 1 0 0.0312 0.0308 0.00237 0.137
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 10 4 0.7143 0.1207 0.40630 0.882
6 4 6 0.2857 0.1207 0.08834 0.524
18 1 3 0.0714 0.0688 0.00452 0.275
24 1 0 0.0714 0.0688 0.00452 0.275circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 46, number of events= 45
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.7342 2.0839 0.3380 2.172 0.0298 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 2.084 0.4799 1.074 4.042
Concordance= 0.591 (se = 0.043 )
Likelihood ratio test= 5.1 on 1 df, p=0.02
Wald test = 4.72 on 1 df, p=0.03
Score (logrank) test = 4.9 on 1 df, p=0.03cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.08 (1.07-4.04); p = 0.03"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 0.18482, df = 1, p-value = 0.6673fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.3043
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.05860791 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Recurrence Recurrence
TRUE 1 13
FALSE 0 32table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 46, number of events= 45
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 0.9147 2.4961 0.3739 2.447 0.0144 *
GenderMale -0.4952 0.6095 0.3597 -1.377 0.1686
Age.Group≥70 0.1691 1.1843 0.3357 0.504 0.6145
ECOG1 NA NA 0.0000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 2.4961 0.4006 1.1996 5.194
GenderMale 0.6095 1.6408 0.3011 1.233
Age.Group≥70 1.1843 0.8444 0.6133 2.287
ECOG1 NA NA NA NA
Concordance= 0.637 (se = 0.047 )
Likelihood ratio test= 7 on 3 df, p=0.07
Wald test = 6.36 on 3 df, p=0.1
Score (logrank) test = 6.56 on 3 df, p=0.09#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 46, number of events= 45
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -0.9147 0.4006 0.3739 -2.447 0.0144 *
GenderMale -0.4952 0.6095 0.3597 -1.377 0.1686
Age.Group≥70 0.1691 1.1843 0.3357 0.504 0.6145
ECOG1 NA NA 0.0000 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.4006 2.4961 0.1925 0.8336
GenderMale 0.6095 1.6408 0.3011 1.2335
Age.Group≥70 1.1843 0.8444 0.6133 2.2868
ECOG1 NA NA NA NA
Concordance= 0.637 (se = 0.047 )
Likelihood ratio test= 7 on 3 df, p=0.07
Wald test = 6.36 on 3 df, p=0.1
Score (logrank) test = 6.56 on 3 df, p=0.09#DFS by ACT treatment in MRD positive - Stage IV no NAC-treated
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ACT, data = circ_data)
n events median 0.95LCL 0.95UCL
ACT=FALSE 28 27 2.8 1.12 3.52
ACT=TRUE 26 15 14.2 5.92 NAevent_summary <- circ_data %>%
group_by(ACT) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage IV No NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))Call: survfit(formula = surv_object ~ ACT, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
ACT=FALSE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 14 14 0.500 0.0945 0.306 0.666
6 4 10 0.143 0.0661 0.045 0.295
ACT=TRUE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
3 24 2 0.923 0.0523 0.726 0.98
6 16 7 0.650 0.0944 0.434 0.80
18 7 6 0.367 0.1031 0.176 0.56
24 5 0 0.367 0.1031 0.176 0.56circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT, data = circ_data)
n= 54, number of events= 42
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.9565 7.0742 0.3975 4.922 8.55e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 7.074 0.1414 3.246 15.42
Concordance= 0.714 (se = 0.025 )
Likelihood ratio test= 28.96 on 1 df, p=7e-08
Wald test = 24.23 on 1 df, p=9e-07
Score (logrank) test = 31.08 on 1 df, p=2e-08cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 7.07 (3.25-15.42); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 9.57, df = 1, p-value = 0.001978fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.0007475
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
2.333943 878.542882
sample estimates:
odds ratio
18.79777 print(contingency_table)
No Recurrence Recurrence
TRUE 11 15
FALSE 1 27table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ACT vs Observation",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 54, number of events= 42
coef exp(coef) se(coef) z Pr(>|z|)
ACTFALSE 1.998062 7.374747 0.405506 4.927 8.34e-07 ***
GenderMale -0.173869 0.840407 0.330194 -0.527 0.598
Age.Group≥70 0.001981 1.001983 0.319386 0.006 0.995
ECOG1 -0.038826 0.961919 0.615096 -0.063 0.950
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTFALSE 7.3747 0.1356 3.3310 16.327
GenderMale 0.8404 1.1899 0.4400 1.605
Age.Group≥70 1.0020 0.9980 0.5358 1.874
ECOG1 0.9619 1.0396 0.2881 3.212
Concordance= 0.728 (se = 0.032 )
Likelihood ratio test= 29.24 on 4 df, p=7e-06
Wald test = 24.52 on 4 df, p=6e-05
Score (logrank) test = 31.36 on 4 df, p=3e-06#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ACT + Gender + Age.Group + ECOG,
data = circ_data)
n= 54, number of events= 42
coef exp(coef) se(coef) z Pr(>|z|)
ACTTRUE -1.998062 0.135598 0.405506 -4.927 8.34e-07 ***
GenderMale -0.173869 0.840407 0.330194 -0.527 0.598
Age.Group≥70 0.001981 1.001983 0.319386 0.006 0.995
ECOG1 -0.038826 0.961919 0.615096 -0.063 0.950
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ACTTRUE 0.1356 7.375 0.06125 0.3002
GenderMale 0.8404 1.190 0.43998 1.6053
Age.Group≥70 1.0020 0.998 0.53579 1.8738
ECOG1 0.9619 1.040 0.28812 3.2115
Concordance= 0.728 (se = 0.032 )
Likelihood ratio test= 29.24 on 4 df, p=7e-06
Wald test = 24.52 on 4 df, p=6e-05
Score (logrank) test = 31.36 on 4 df, p=3e-06#DFS by ctDNA Clearance ACT-treated at 3 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
674 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 100 42 27.53 18.07 NA
ctDNA.Dynamics=2 71 64 4.14 3.22 5.55event_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
674 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 23.0000 41.0000 0.5217 0.0571 0.4047 0.6263
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 5.0000 64.0000 0.0913 0.0355 0.0372 0.1753 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 171, number of events= 106
(674 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 1.6822 5.3775 0.2055 8.187 2.67e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 5.378 0.186 3.595 8.044
Concordance= 0.716 (se = 0.018 )
Likelihood ratio test= 67.62 on 1 df, p=<2e-16
Wald test = 67.03 on 1 df, p=3e-16
Score (logrank) test = 81.18 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 5.38 (3.59-8.04); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 38.82, df = 1, p-value = 4.647e-10fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 4.578e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
5.03289 35.47066
sample estimates:
odds ratio
12.42856 print(contingency_table)
No Recurrence Recurrence
Clearance 58 42
No Clearance 7 64table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA clearance at 3 months",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#OS by ctDNA Clearance ACT-treated at 3 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$OS.3mo.months>=0,]
survfit(Surv(time = circ_data$OS.3mo.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.3mo.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
674 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 100 7 NA NA NA
ctDNA.Dynamics=2 71 16 41.6 31.9 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.3mo.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
674 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 39.0000 6.0000 0.8936 0.0423 0.7738 0.9519
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 24.000 13.000 0.716 0.070 0.553 0.829 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 171, number of events= 23
(674 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 1.3251 3.7627 0.4583 2.892 0.00383 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 3.763 0.2658 1.533 9.238
Concordance= 0.689 (se = 0.047 )
Likelihood ratio test= 9.18 on 1 df, p=0.002
Wald test = 8.36 on 1 df, p=0.004
Score (logrank) test = 9.65 on 1 df, p=0.002cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 3.76 (1.53-9.24); p = 0.004"circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.3252, df = 1, p-value = 0.0068fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.005462
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.387371 11.743872
sample estimates:
odds ratio
3.833332 print(contingency_table)
Alive Deceased
Clearance 93 7
No Clearance 55 16table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA clearance at 3 months",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by ctDNA Clearance ACT-treated at 6 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event) ~
ctDNA.Dynamics, data = circ_data)
732 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 77 27 NA 17.74 NA
ctDNA.Dynamics=2 35 34 2.4 1.61 3.68event_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
732 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 61 14 0.816 0.0445 0.709 0.886
24 15 13 0.602 0.0625 0.469 0.712
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6.0000 5.0000 29.0000 0.1607 0.0638 0.0609 0.3028 circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 112, number of events= 61
(732 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 2.4088 11.1201 0.3069 7.848 4.24e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 11.12 0.08993 6.093 20.29
Concordance= 0.729 (se = 0.023 )
Likelihood ratio test= 64.06 on 1 df, p=1e-15
Wald test = 61.58 on 1 df, p=4e-15
Score (logrank) test = 88.6 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 11.12 (6.09-20.29); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 34.928, df = 1, p-value = 3.42e-09fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 7.575e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
9.188775 2571.010947
sample estimates:
odds ratio
60.91302 print(contingency_table)
No Recurrence Recurrence
Clearance 50 27
No Clearance 1 34table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA clearance at 6 months",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#OS by ctDNA Clearance ACT-treated at 6 months - all stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
))
circ_data <- circ_data[circ_data$OS.6mo.months>=0,]
survfit(Surv(time = circ_data$OS.6mo.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.6mo.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
732 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 77 3 NA NA NA
ctDNA.Dynamics=2 36 7 39 27.9 NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.6mo.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(6, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
732 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 72 0 1.000 0.0000 NA NA
24 27 2 0.966 0.0236 0.871 0.991
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
6 22 3 0.896 0.0571 0.710 0.966
24 8 2 0.791 0.0863 0.558 0.910circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 113, number of events= 10
(732 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsNo Clearance 1.8445 6.3252 0.7088 2.602 0.00926 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsNo Clearance 6.325 0.1581 1.577 25.37
Concordance= 0.747 (se = 0.071 )
Likelihood ratio test= 7.27 on 1 df, p=0.007
Wald test = 6.77 on 1 df, p=0.009
Score (logrank) test = 8.89 on 1 df, p=0.003cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.33 (1.58-25.37); p = 0.009"circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 5.5507, df = 1, p-value = 0.01847fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.01138
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.232449 37.417947
sample estimates:
odds ratio
5.846658 print(contingency_table)
Alive Deceased
Clearance 74 3
No Clearance 29 7table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA clearance at 6 months",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Sankey plot for 3 months to 6 months ctDNA clearance
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA + MRD window [185] ACT-treated #ADD8E6
#ctDNA + MRD window [151] Not treated #808080
#ACT-treated [100] ctDNA Clearance at 3 months #87EA86
#ACT-treated [71] No Clearance at 3 months #E67272
#ACT-treated [14] No 3 months time point #808080
#ctDNA Clearance at 3 months [7] ctDNA + at 6 months #E67272
#ctDNA Clearance at 3 months [64] ctDNA - at 6 months #87EA86
#ctDNA Clearance at 3 months [29] No 6 months time point #808080
#No Clearance at 3 months [27] ctDNA + at 6 months #E67272
#No Clearance at 3 months [11] ctDNA - at 6 months #87EA86
#No Clearance at 3 months [33] No 6 months time point #808080
#No 3 months time point [2] ctDNA + at 6 months #E67272
#No 3 months time point [2] ctDNA - at 6 months #87EA86
#No 3 months time point [10] No 6 months time point #808080#Number of MRD positive patients & ctDNA clearance on ACT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
# Count the number of MRD positive patients
number_of_positive_patients <- sum(circ_datadf$ctDNA.MRD == "POSITIVE", na.rm = TRUE)
print(paste("Number of MRD positive patients:", number_of_positive_patients))[1] "Number of MRD positive patients: 336"# Count the number & percentage of MRD positive patients treated with ACT
positive_subset <- sum(circ_datadf$ACT == "TRUE" & circ_datadf$ctDNA.MRD == "POSITIVE", na.rm = TRUE)
print(paste("Number of MRD positive patients treated with ACT:", positive_subset))[1] "Number of MRD positive patients treated with ACT: 185"percentage_positive_for_both <- (positive_subset / number_of_positive_patients) * 100
print(paste("Percentage of MRD positive patients treated with ACT:", percentage_positive_for_both, "%"))[1] "Percentage of MRD positive patients treated with ACT: 55.0595238095238 %"# Count the number & percentage of patients with ctDNA clearance post-ACT
clearance_postACT <- sum(
(circ_datadf$ACT == "TRUE") &
(circ_datadf$ctDNA.MRD == "POSITIVE") &
(circ_datadf$Clearance.Event == "TRUE"),
na.rm = TRUE
)
print(paste("Number of patients with ctDNA Clearance post-ACT:", clearance_postACT))[1] "Number of patients with ctDNA Clearance post-ACT: 126"percentage_clearance <- (clearance_postACT / positive_subset) * 100
print(paste("ctDNA Clearance post-ACT:", percentage_clearance, "%"))[1] "ctDNA Clearance post-ACT: 68.1081081081081 %"# Count the number of patients with subsequent timepoints available
clearance_subset <- sum(
(circ_datadf$ACT == "TRUE") &
(circ_datadf$ctDNA.MRD == "POSITIVE") &
(circ_datadf$Transient.Clearance == "TRUE" | circ_datadf$Transient.Clearance == "FALSE"),
na.rm = TRUE
)
print(paste("Number of patients with subsequent timepoints available:", clearance_subset))[1] "Number of patients with subsequent timepoints available: 126"# Count the number & percentage of patients with sustained clearance
clearance_sustained <- sum(
(circ_datadf$ACT == "TRUE") &
(circ_datadf$ctDNA.MRD == "POSITIVE") &
(circ_datadf$Transient.Clearance == "FALSE"),
na.rm = TRUE
)
print(paste("Number of patients with sustained clearance:", clearance_sustained))[1] "Number of patients with sustained clearance: 68"percentage_sustained_clearance <- (clearance_sustained / clearance_subset) * 100
print(paste("Sustained ctDNA Clearance:", percentage_sustained_clearance, "%"))[1] "Sustained ctDNA Clearance: 53.968253968254 %"# Count the number & percentage of patients with transient clearance
clearance_transient <- sum(
(circ_datadf$ACT == "TRUE") &
(circ_datadf$ctDNA.MRD == "POSITIVE") &
(circ_datadf$Transient.Clearance == "TRUE"),
na.rm = TRUE
)
print(paste("Number of patients with transient clearance:", clearance_transient))[1] "Number of patients with transient clearance: 58"percentage_transient_clearance <- (clearance_transient / clearance_subset) * 100
print(paste("Transient ctDNA Clearance:", percentage_transient_clearance, "%"))[1] "Transient ctDNA Clearance: 46.031746031746 %"#Sankey plot for Sustained vs Transient Clearance
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA + MRD window [185] ACT-treated #ADD8E6
#ctDNA + MRD window [151] Not treated #808080
#ACT-treated [126] ctDNA post-MRD Clearance #87EA86
#ACT-treated [55] No Clearance #E67272
#ACT-treated [4] No post-MRD time point #808080
#No Clearance [55] No Clearance analysis #E67272
#ctDNA post-MRD Clearance [126] Available post-MRD Timepoints #ADD8E66
#Available post-MRD Timepoints [68] Sustained Clearance #7393B3
#Available post-MRD Timepoints [58] Transient Clearance #87EA86#DFS by ctDNA Clearance post-MRD - 3 Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.Clearance!="",]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.Clearance, data = circ_data)
131 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Clearance=No Clearance 55 55 4.83 4.53 5.45
ctDNA.Clearance=Sustained 68 7 NA NA NA
ctDNA.Clearance=Transient 58 50 12.88 10.38 15.64event_summary <- circ_data %>%
group_by(ctDNA.Clearance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue","green"), title="DFS - ctDNA Clearance post-MRD | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("No Clearance", "Sustained", "Transient"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))Call: survfit(formula = surv_object ~ ctDNA.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
131 observations deleted due to missingness
ctDNA.Clearance=No Clearance
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12.00000 1.00000 54.00000 0.01818 0.01802 0.00149 0.08474
ctDNA.Clearance=Sustained
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 57 5 0.925 0.0321 0.830 0.968
18 48 1 0.909 0.0354 0.809 0.958
24 31 1 0.890 0.0394 0.783 0.946
ctDNA.Clearance=Transient
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 28 27 0.5212 0.0668 0.38358 0.642
18 6 18 0.1500 0.0527 0.06542 0.267
24 1 4 0.0333 0.0312 0.00294 0.137circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Clearance, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Clearance, data = circ_data)
n= 181, number of events= 112
(131 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.ClearanceTransient 2.9815 19.7182 0.4229 7.051 1.78e-12 ***
ctDNA.ClearanceNo Clearance 4.8264 124.7631 0.4565 10.573 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.ClearanceTransient 19.72 0.050715 8.608 45.17
ctDNA.ClearanceNo Clearance 124.76 0.008015 50.996 305.24
Concordance= 0.83 (se = 0.017 )
Likelihood ratio test= 207 on 2 df, p=<2e-16
Wald test = 129.1 on 2 df, p=<2e-16
Score (logrank) test = 234.2 on 2 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for No Clearance
z_value <- cox_fit_summary$coefficients["ctDNA.ClearanceNo Clearance", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for No Clearance:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for No Clearance: 3.965447420453033828286722102521361568372303293428849184735806408930601077610622e-26"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Clearance, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 125.14, df = 2, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: two.sidedprint(contingency_table)
No Recurrence Recurrence
Sustained 61 7
Transient 8 50
No Clearance 0 55table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA Dynamics post-MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 6.689566022898946601444361306683350082631646213567832590100460138872917777164151e-28"#Levels of MRD MTM/mL in Clearance post-MRD log10 transformation
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!is.na(circ_data$ctDNA.Clearance) & circ_data$ctDNA.Clearance != "",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)
# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
median_p_MRD_MTM <- aggregate(p_MRD_MTM ~ ctDNA.Clearance, data = circ_data, FUN = median)
print(median_p_MRD_MTM)
# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Clearance, y=p_MRD_MTM, fill=ctDNA.Clearance)) +
geom_violin(trim=FALSE) +
scale_fill_manual(values=c("Sustained"="lightblue", "Transient"="lightgreen", "No Clearance"="salmon")) +
geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
labs(title="MRD MTM/mL | Clearance post-MRD", x="Clearance post-MRD", y="MRD MTM/mL") +
theme_minimal() +
theme(legend.position="none")
m3_1v2 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
data = circ_data[circ_data$ctDNA.Clearance %in% c("Sustained", "Transient"), ],
na.rm = TRUE)
print(m3_1v2)
Wilcoxon rank sum test with continuity correction
data: p_MRD_MTM by ctDNA.Clearance
W = 1946, p-value = 0.9007
alternative hypothesis: true location shift is not equal to 0m3_1v3 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
data = circ_data[circ_data$ctDNA.Clearance %in% c("Sustained", "No Clearance"), ],
na.rm = TRUE)
print(m3_1v3)
Wilcoxon rank sum test with continuity correction
data: p_MRD_MTM by ctDNA.Clearance
W = 906, p-value = 9.529e-07
alternative hypothesis: true location shift is not equal to 0m3_2v3 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
data = circ_data[circ_data$ctDNA.Clearance %in% c("Transient", "No Clearance"), ],
na.rm = TRUE)
print(m3_2v3)
Wilcoxon rank sum test with continuity correction
data: p_MRD_MTM by ctDNA.Clearance
W = 782, p-value = 3.052e-06
alternative hypothesis: true location shift is not equal to 0#Percentages of recurred transient clearance that return positive
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$ACT=="TRUE",]
circ_data <- circ_data[circ_data$Clearance.Event=="TRUE",]
circ_data <- circ_data[circ_data$DFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- subset(circ_data, !is.na(Transient.Clearance))
circ_data <- circ_data[circ_data$Transient.Clearance=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
# Convert days to months
circ_data$p_drelReturned_months <- circ_data$p_drelReturned / 30.437
# Define the intervals: 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(3, 6, 9, 12, 15, 18, 21, 24, 27)
labels <- c("3-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")
# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$p_drelReturned_months, breaks = breaks, labels = labels, right = FALSE)
# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)
3-6m 6-9m 9-12m 12-15m 15-18m 18-21m 21-24m >24m
7 23 8 4 6 0 2 0 # Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)
# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)
# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)
# Print the summary
print(interval_summary)
# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)
# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages)
bp <- barplot(interval_percentages,
main="Distribution of ctDNA Intervals",
xlab="Intervals",
ylab="Percentage",
col="lightblue",
ylim=c(0, 100),
las=2) # las=2 makes the axis labels perpendicular to the axis
# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
#OS by ctDNA Clearance post-MRD - 3 Groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Clearance.Cohort=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
surv_object <- Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Clearance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) ~
ctDNA.Clearance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Clearance=No Clearance 55 17 32.5 23.9 NA
ctDNA.Clearance=Sustained 68 0 NA NA NA
ctDNA.Clearance=Transient 58 7 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Clearance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)KM_curve <- survfit(surv_object ~ ctDNA.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue","green"), title="OS - ctDNA Clearance post-MRD | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("No Clearance", "Sustained", "Transient"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))Call: survfit(formula = surv_object ~ ctDNA.Clearance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Clearance=No Clearance
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 27 7 0.839 0.0570 0.687 0.921
18 21 4 0.706 0.0776 0.524 0.829
24 14 2 0.617 0.0895 0.419 0.765
ctDNA.Clearance=Sustained
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 61 0 1 0 NA NA
18 54 0 1 0 NA NA
24 37 0 1 0 NA NA
ctDNA.Clearance=Transient
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 44 0 1.000 0.0000 NA NA
18 34 1 0.972 0.0274 0.819 0.996
24 18 4 0.823 0.0747 0.615 0.925circ_data$ctDNA.Clearance <- as.factor(circ_data$ctDNA.Clearance)
circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
cox_fit <- coxphf(surv_object ~ ctDNA.Clearance, data=circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Clearance, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.ClearanceTransient 3.239402 1.510369 25.51846 3.099164 3314.725 11.55743 6.747909e-04
ctDNA.ClearanceNo Clearance 4.325656 1.484378 75.61513 10.218215 9650.929 34.76657 3.717015e-09
Likelihood ratio test=34.78097 on 2 df, p=2.80161e-08, n=181
Wald test = 12.97638 on 2 df, p = 0.001521303
Covariance-Matrix:
ctDNA.ClearanceTransient ctDNA.ClearanceNo Clearance
ctDNA.ClearanceTransient 2.281214 2.138730
ctDNA.ClearanceNo Clearance 2.138730 2.203378circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Clearance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 25.362, df = 2, p-value = 3.109e-06fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.67e-07
alternative hypothesis: two.sidedprint(contingency_table)
Alive Deceased
Sustained 68 0
Transient 51 7
No Clearance 38 17table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA Dynamics post-MRD",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Percentages of MRD negative with molecular recurrence (returned positive) post-MRD
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
# Convert days to months
#circ_data$PostMRDPos.months <- circ_data$PostMRDPos / 30.437
# Define the intervals: 0-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(0, 6, 9, 12, 15, 18, 21, 24, 48)
labels <- c("0-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")
# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$PostMRDPos.months, breaks = breaks, labels = labels, right = FALSE)
# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)
0-6m 6-9m 9-12m 12-15m 15-18m 18-21m 21-24m >24m
77 35 23 2 20 1 7 0 # Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)
# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)
# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)
# Calculate the total number of observations
total_observations <- sum(interval_counts)
# Add the total number of observations to the summary
interval_summary$TotalObservations <- c(rep(NA, length(interval_counts)-1), total_observations)
# Print the summary with total observations
print(interval_summary)
# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)
# Combine the counts, percentages, and cumulative percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages, TotalObservations = c(rep(NA, length(interval_counts)-1), total_observations))
bp <- barplot(interval_percentages,
main="Distribution of ctDNA Intervals",
xlab="Intervals",
ylab="Percentage",
col="lightblue",
ylim=c(0, 100),
las=2) # las=2 makes the axis labels perpendicular to the axis
# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
print(interval_summary)#OS by ctDNA MRD positive vs ctDNA negative with molecular recurrence at Surveillance - 3 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics
= case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
321 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 1294 13 NA NA NA
ctDNA.Dynamics=2 159 15 NA NA NA
ctDNA.Dynamics=3 336 52 43.4 NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","red"), title="OS - ctDNA MRD Pos vs Neg with Molecular Recurrence at Surveillance Window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
321 observations deleted due to missingness
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 1137 0 1.000 0.0000 NA NA
24 640 5 0.995 0.0023 0.988 0.998
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 126 2 0.987 0.00909 0.949 0.997
24 58 8 0.900 0.03138 0.817 0.946
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 228 17 0.942 0.0136 0.909 0.964
24 119 20 0.837 0.0258 0.778 0.881circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 1789, number of events= 80
(321 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsMolecular Recurrence 2.4747 11.8787 0.3796 6.519 7.09e-11 ***
ctDNA.DynamicsctDNA MRD Positive 3.0205 20.5007 0.3103 9.734 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsMolecular Recurrence 11.88 0.08418 5.644 25.00
ctDNA.DynamicsctDNA MRD Positive 20.50 0.04878 11.160 37.66
Concordance= 0.833 (se = 0.019 )
Likelihood ratio test= 138.3 on 2 df, p=<2e-16
Wald test = 94.79 on 2 df, p=<2e-16
Score (logrank) test = 182.9 on 2 df, p=<2e-16circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 140.83, df = 2, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: two.sidedprint(contingency_table)
Alive Deceased
All-time negative 1281 13
Molecular Recurrence 144 15
ctDNA MRD Positive 284 52table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Molecular Recurrence at Surveillance Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 2.627721284911699786619385222026550649715496564590507630695278420622264551055174e-31"rm(list=ls()) #repeat to compare Molecular Recurrence vs ctDNA MRD positive
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
ctDNA.MRD == "POSITIVE" ~ 3
))
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","3","1"), labels = c("Molecular Recurrence", "ctDNA MRD Positive", "All-time negative"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 1789, number of events= 80
(321 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.DynamicsctDNA MRD Positive 0.54572 1.72584 0.29355 1.859 0.063 .
ctDNA.DynamicsAll-time negative -2.47474 0.08418 0.37964 -6.519 7.09e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.DynamicsctDNA MRD Positive 1.72584 0.5794 0.9708 3.0681
ctDNA.DynamicsAll-time negative 0.08418 11.8787 0.0400 0.1772
Concordance= 0.833 (se = 0.019 )
Likelihood ratio test= 138.3 on 2 df, p=<2e-16
Wald test = 94.79 on 2 df, p=<2e-16
Score (logrank) test = 182.9 on 2 df, p=<2e-16#Time-dependent analysis - Molecular Recurrence patients Landmark from molecular recurrence with RFS event as outcome
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("Galaxy 36mo Time dependent.csv")
dt_final <- dt_final[!is.na(dt_final$tstart4), ]
dt_final$tstart4 <- as.numeric(as.character(dt_final$tstart4))
dt_final$tstop4 <- as.numeric(as.character(dt_final$tstop4))Warning: NAs introduced by coerciondatatable(dt_final, filter = "top")fit <- coxph(Surv(tstart4, tstop4, rfs_event) ~ biomarker_status,
data = dt_final)Warning in Surv(tstart4, tstop4, rfs_event) :
Stop time must be > start time, NA createdsummary(fit)Call:
coxph(formula = Surv(tstart4, tstop4, rfs_event) ~ biomarker_status,
data = dt_final)
n= 272, number of events= 169
(63 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
biomarker_statusPOSITIVE 1.1143 3.0474 0.2638 4.224 2.4e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
biomarker_statusPOSITIVE 3.047 0.3281 1.817 5.111
Concordance= 0.543 (se = 0.01 )
Likelihood ratio test= 21.91 on 1 df, p=3e-06
Wald test = 17.84 on 1 df, p=2e-05
Score (logrank) test = 19.48 on 1 df, p=1e-05summary_fit <- summary(fit)
hr <- summary_fit$coef[1, "exp(coef)"]
ci_lower <- summary_fit$conf.int[1, "lower .95"]
ci_upper <- summary_fit$conf.int[1, "upper .95"]
p_value <- summary_fit$coef[1, "Pr(>|z|)"]
formatted_p_value <- ifelse(p_value < 0.0001, "<0.0001", sprintf("%.3f", p_value))
result_line <- sprintf("HR = %.2f (%.2f–%.2f); P %s", hr, ci_lower, ci_upper, formatted_p_value)
print(result_line)[1] "HR = 3.05 (1.82–5.11); P <0.0001"#OS by timing of molecular recurrence in ctDNA MRD negative - 3 groups
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
PostMRDPos.months >= 0 & PostMRDPos.months < 6 ~ 1,
PostMRDPos.months >= 6 & PostMRDPos.months < 12 ~ 2,
PostMRDPos.months >= 12 & PostMRDPos.months < 24 ~ 3
))
circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) ~
ctDNA.Dynamics, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Dynamics=1 77 11 NA 38.9 NA
ctDNA.Dynamics=2 58 5 NA NA NA
ctDNA.Dynamics=3 30 0 NA NA NAevent_summary <- circ_data %>%
group_by(ctDNA.Dynamics) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="OS - ctDNA MRD Neg with Molecular Recurrence", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("6mo","6-12mo", "12mo"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))Call: survfit(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Dynamics=1
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 59 2 0.972 0.0192 0.894 0.993
24 35 4 0.902 0.0384 0.793 0.955
36 7 4 0.774 0.0693 0.602 0.879
ctDNA.Dynamics=2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 47 0 1.000 0.0000 NA NA
24 17 4 0.847 0.0721 0.635 0.941
36 4 1 0.786 0.0887 0.547 0.909
ctDNA.Dynamics=3
time n.risk n.event survival std.err lower 95% CI upper 95% CI
12 30 0 1 0 1 1
24 16 0 1 0 NA NA
36 3 0 1 0 NA NAcirc_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","2","1"), labels = c(">12 months","6-12 months", "<6 months"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data = circ_data, maxstep = 0.5, maxit = 100)
summary(cox_fit)coxphf(formula = surv_object ~ ctDNA.Dynamics, data = circ_data,
maxit = 100, maxstep = 0.5)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
ctDNA.Dynamics6-12 months 2.025644 1.546344 7.580989 0.8572701 995.9189 3.209661 0.07320463
ctDNA.Dynamics<6 months 2.334475 1.511804 10.324038 1.3459559 1325.4489 5.531177 0.01868054
Likelihood ratio test=5.532375 on 2 df, p=0.06290137, n=165
Wald test = 2.538202 on 2 df, p = 0.2810842
Covariance-Matrix:
ctDNA.Dynamics6-12 months ctDNA.Dynamics<6 months
ctDNA.Dynamics6-12 months 2.391179 2.190536
ctDNA.Dynamics<6 months 2.190536 2.285550circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 5.1498, df = 2, p-value = 0.07616fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.05623
alternative hypothesis: two.sidedprint(contingency_table)
Alive Deceased
>12 months 30 0
6-12 months 53 5
<6 months 66 11table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Timing of Molecular Recurrence",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
mutate(ctDNA.Dynamics = case_when(
PostMRDPos.months >= 0 & PostMRDPos.months < 6 ~ 1,
PostMRDPos.months >= 6 & PostMRDPos.months < 12 ~ 2,
PostMRDPos.months >= 12 & PostMRDPos.months < 24 ~ 3
))
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","1"), labels = c("6-12 months", "<6 months"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Dynamics, data = circ_data)
n= 135, number of events= 16
(1729 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.Dynamics<6 months 0.3594 1.4325 0.5409 0.664 0.506
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.Dynamics<6 months 1.432 0.6981 0.4962 4.135
Concordance= 0.569 (se = 0.061 )
Likelihood ratio test= 0.46 on 1 df, p=0.5
Wald test = 0.44 on 1 df, p=0.5
Score (logrank) test = 0.45 on 1 df, p=0.5#DFS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 1481 89 NA NA NA
ctDNA.Surveillance=POSITIVE 310 261 8.47 7.09 8.74event_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 565 81 0.932 0.00756 0.915 0.945
30 311 5 0.922 0.00878 0.902 0.937
36 113 2 0.915 0.00975 0.894 0.933
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 14 257 0.0893 0.0197 0.0556 0.133
30 4 2 0.0649 0.0213 0.0314 0.115circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 1791, number of events= 350
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 3.5133 33.5603 0.1289 27.26 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 33.56 0.0298 26.07 43.2
Concordance= 0.835 (se = 0.01 )
Likelihood ratio test= 875 on 1 df, p=<2e-16
Wald test = 743.2 on 1 df, p=<2e-16
Score (logrank) test = 1682 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 1024)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 1.214551525691134042871085988135688455034499263643110646866372274500527478628517683904586629283185191412263408625381965570040271497304617938067505737554391250773567449046390689304661077575604764646557664393992986262331141367686739919656664044919499358928083657617110519896493480065999595002164834207317118429762e-163"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 991.63, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
56.55705 123.45382
sample estimates:
odds ratio
82.94443 print(contingency_table)
No Recurrence Recurrence
Negative 1392 89
Positive 49 261table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the Surveillance Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.187618849631467104902804273208187990563563535683764640207640765631941850017620e-217"#OS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 1481 13 NA NA NA
ctDNA.Surveillance=POSITIVE 313 41 41.8 37.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 686 7 0.993 0.00288 0.984 0.997
30 384 5 0.982 0.00552 0.967 0.990
36 123 1 0.979 0.00608 0.963 0.989
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 102 31 0.832 0.0294 0.765 0.881
30 60 4 0.792 0.0343 0.715 0.850
36 14 4 0.705 0.0571 0.577 0.801circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 1794, number of events= 54
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.9708 19.5075 0.3189 9.317 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 19.51 0.05126 10.44 36.44
Concordance= 0.825 (se = 0.028 )
Likelihood ratio test= 105.6 on 1 df, p=<2e-16
Wald test = 86.8 on 1 df, p=<2e-16
Score (logrank) test = 171.6 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 1.197828328837432986086908362318426280923213299603172307580225877386958159287885e-20"# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 19.51 (10.44-36.44); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 128.04, df = 1, p-value < 2.2e-16fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value < 2.2e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
8.778118 35.028967
sample estimates:
odds ratio
16.97861 print(contingency_table)
Alive Deceased
Negative 1468 13
Positive 272 41table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the Surveillance Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.099706546214144935843163919734935470119396368846637974186053403399393570475089e-29"#Multivariate cox regression at Surveillance Window for DFS - All stages Landmark 10 weeks
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Left-sided colon", "Right-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data)
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance + Gender + Age.Group +
PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data = circ_data)
n= 1552, number of events= 243
(239 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePositive 3.78959 44.23832 0.16417 23.083 < 2e-16 ***
GenderMale 0.51108 1.66710 0.13686 3.734 0.000188 ***
Age.Group>70 -0.01009 0.98996 0.13840 -0.073 0.941857
PrimSiteRight-sided colon -0.02687 0.97349 0.14265 -0.188 0.850585
ECOG1 -0.51425 0.59795 0.21677 -2.372 0.017679 *
pTT3-T4 0.09072 1.09497 0.35985 0.252 0.800956
pNN1-N2 0.40998 1.50678 0.15843 2.588 0.009662 **
MSIMSI-High -1.81099 0.16349 0.60363 -3.000 0.002698 **
BRAF.V600EV600E 0.61736 1.85403 0.32144 1.921 0.054784 .
RASMutant 0.37032 1.44820 0.13383 2.767 0.005654 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePositive 44.2383 0.0226 32.06694 61.0295
GenderMale 1.6671 0.5998 1.27486 2.1800
Age.Group>70 0.9900 1.0101 0.75476 1.2984
PrimSiteRight-sided colon 0.9735 1.0272 0.73605 1.2875
ECOG1 0.5979 1.6724 0.39097 0.9145
pTT3-T4 1.0950 0.9133 0.54087 2.2167
pNN1-N2 1.5068 0.6637 1.10457 2.0555
MSIMSI-High 0.1635 6.1165 0.05008 0.5337
BRAF.V600EV600E 1.8540 0.5394 0.98742 3.4812
RASMutant 1.4482 0.6905 1.11408 1.8825
Concordance= 0.886 (se = 0.012 )
Likelihood ratio test= 755.9 on 10 df, p=<2e-16
Wald test = 584 on 10 df, p=<2e-16
Score (logrank) test = 1553 on 10 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePositive", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 6.79046242980828407778347722517594258672299437962476219045684884610635807239003942154501178488999766155012921415351152445782542204539645762093295358348123462e-118"#OS by ctDNA at the MRD Window - pts with Radiological Recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
1 observation deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 219 22 NA NA NA
ctDNA.MRD=POSITIVE 263 52 43.4 36.8 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Radiological Recurrence | ctDNA MRD window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 36))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
1 observation deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 110 12 0.926 0.0209 0.873 0.958
36 21 9 0.830 0.0364 0.744 0.889
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 84 37 0.783 0.0334 0.708 0.840
36 13 13 0.626 0.0490 0.522 0.714circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 482, number of events= 74
(1 observation deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 0.9954 2.7059 0.2557 3.893 9.89e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 2.706 0.3696 1.639 4.466
Concordance= 0.631 (se = 0.027 )
Likelihood ratio test= 16.67 on 1 df, p=4e-05
Wald test = 15.16 on 1 df, p=1e-04
Score (logrank) test = 16.43 on 1 df, p=5e-05cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 2.71 (1.64-4.47); p = 0"circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.9661, df = 1, p-value = 0.004766fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.00342
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.260726 3.958921
sample estimates:
odds ratio
2.203332 print(contingency_table)
Alive Deceased
Negative 197 22
Positive 211 52table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#OS by ctDNA at the MRD Window - pts with Radiological Recurrence Sites
# Define the function to analyze each recurrence site and extract HR values
analyze_site <- function(site) {
circ_data_site <- circ_data %>% filter(grepl(site, RelSite, ignore.case = TRUE))
circ_data_site <- circ_data_site[circ_data_site$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
surv_object <- Surv(time = circ_data_site$OS.MRD.months, event = circ_data_site$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = circ_data_site)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", format.pval(p_value, digits = 3))
return(list(HR = HR, lower_CI = lower_CI, upper_CI = upper_CI, p_value = p_value, site = site, label_text = label_text))
}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$RFS.Event == "TRUE",]
recurrence_sites <- c("liver", "lung", "peritoneum", "lymph node")
results <- lapply(recurrence_sites, analyze_site)
forest_data <- do.call(rbind, lapply(results, function(res) {
data.frame(
site = res$site,
HR = res$HR,
lower_CI = res$lower_CI,
upper_CI = res$upper_CI,
label_text = res$label_text
)
}))
forest_data$site <- factor(forest_data$site, levels = c("liver", "lung", "peritoneum", "lymph node"))
forest_plot <- ggplot(forest_data, aes(x = site, y = HR, ymin = lower_CI, ymax = upper_CI)) +
geom_pointrange() +
geom_text(aes(label = label_text), hjust = -0.1, vjust = -0.5) +
geom_hline(yintercept = 1, linetype = "dashed") +
coord_flip() +
scale_y_continuous(breaks = seq(1, max(forest_data$upper_CI) + 1, by = 2), expand = c(0, 0), limits = c(0, max(forest_data$upper_CI) + 1)) +
labs(x = "Recurrence Site", y = "HR for OS between ctDNA MRD positive vs negative") +
theme_minimal()
# Define the function to analyze each recurrence site and extract HR values
analyze_site <- function(site) {
circ_data_site <- circ_data %>% filter(grepl(site, RelSite, ignore.case = TRUE))
circ_data_site <- circ_data_site[circ_data_site$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
site_count <- nrow(circ_data_site)
print(paste("Number of observations for site", site, ":", site_count))
surv_object <- Surv(time = circ_data_site$OS.months, event = circ_data_site$OS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = circ_data_site)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", format.pval(p_value, digits = 3))
return(list(HR = HR, lower_CI = lower_CI, upper_CI = upper_CI, p_value = p_value, site = site, label_text = label_text))
}
# Set working directory and load data
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$RFS.Event == "TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
# Recurrence sites to analyze
recurrence_sites <- c("liver", "lung", "peritoneum", "lymph node")
# Perform analysis for each site
results <- lapply(recurrence_sites, analyze_site)[1] "Number of observations for site liver : 209"
[1] "Number of observations for site lung : 191"
[1] "Number of observations for site peritoneum : 115"
[1] "Number of observations for site lymph node : 66"# Create data frame for forest plot
forest_data <- do.call(rbind, lapply(results, function(res) {
data.frame(
site = res$site,
HR = res$HR,
lower_CI = res$lower_CI,
upper_CI = res$upper_CI,
label_text = res$label_text
)
}))
# Set the order of the levels for the 'site' factor
forest_data$site <- factor(forest_data$site, levels = c("liver", "lung", "peritoneum", "lymph node"))
# Create forest plot
forest_plot <- ggplot(forest_data, aes(x = site, y = HR, ymin = lower_CI, ymax = upper_CI)) +
geom_pointrange() +
geom_text(aes(label = label_text), hjust = -0.1, vjust = -0.5) +
geom_hline(yintercept = 1, linetype = "dashed") +
coord_flip() +
scale_y_continuous(breaks = seq(1, max(forest_data$upper_CI) + 1, by = 2), expand = c(0, 0), limits = c(0, max(forest_data$upper_CI) + 1)) +
labs(x = "Recurrence Site", y = "HR for OS between ctDNA MRD positive vs negative") +
theme_minimal()
print(forest_plot)
for (res in results) {
print(res$label_text)
}[1] "HR = 2.43 (1.01-5.86); p = 0.048"
[1] "HR = 2.64 (1.2-5.83); p = 0.016"
[1] "HR = 2.73 (1.31-5.7); p = 0.007"
[1] "HR = 2.67 (0.83-8.55); p = 0.098"#OS by ctDNA at the Surveillance Window - pts with Radiological Recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$OS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 78 2 NA NA NA
ctDNA.Surveillance=POSITIVE 264 41 41.8 37.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Radiological Recurrence | ctDNA Surveillance window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 36))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 48 0 1.000 0.0000 NA NA
36 3 2 0.931 0.0471 0.751 0.982
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 90 31 0.809 0.0325 0.736 0.864
36 14 8 0.680 0.0592 0.548 0.780circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 342, number of events= 43
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 2.1278 8.3962 0.7252 2.934 0.00334 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 8.396 0.1191 2.027 34.78
Concordance= 0.631 (se = 0.015 )
Likelihood ratio test= 16.74 on 1 df, p=4e-05
Wald test = 8.61 on 1 df, p=0.003
Score (logrank) test = 12.36 on 1 df, p=4e-04cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 8.4 (2.03-34.78); p = 0.003"circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 8.0672, df = 1, p-value = 0.004507fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.001475
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.735104 60.847334
sample estimates:
odds ratio
6.962255 print(contingency_table)
Alive Deceased
Negative 76 2
Positive 223 41table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the Surveillance Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Sankey plot for MRD and Surveillance dynamics
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA at the MRD Window [336] ctDNA + MRD window #E67272
#ctDNA at the MRD Window [1773] ctDNA - MRD window #87EA86
#ctDNA at the MRD Window [131] Not available ctDNA MRD window #808080
#ctDNA + MRD window [141] ctDNA + Surveillance window #E67272
#ctDNA + MRD window [70] ctDNA - Surveillance window #87EA86
#ctDNA + MRD window [125] Not available ctDNA Surveillance window #808080
#ctDNA - MRD window [159] ctDNA + Surveillance window #E67272
#ctDNA - MRD window [1294] ctDNA - Surveillance window #87EA86
#ctDNA - MRD window [320] Not available ctDNA Surveillance window #808080
#Not available ctDNA MRD window [13] ctDNA + Surveillance window #E67272
#Not available ctDNA MRD window [117] ctDNA - Surveillance window #87EA86
#Not available ctDNA MRD window [1] Not available ctDNA Surveillance window #808080
#ctDNA + Surveillance window [264] Radiological Recurrence #E67272
#ctDNA + Surveillance window [49] No Recurrence #87EA86
#ctDNA - Surveillance window [78] Radiological Recurrence #E67272
#ctDNA - Surveillance window [1403] No Recurrence #87EA86
#Not available ctDNA Surveillance window [158] Radiological Recurrence #E67272
#Not available ctDNA Surveillance window [288] No Recurrence #87EA86#Percentage of ctDNA MRD Window positivity in pts undergoing post-recurrence curative surgery
rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>%
filter(Eligible == "TRUE" & RFS.Event == "TRUE" & ctDNA.MRD != "")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
positive_rate <- sum(circ_data$ctDNA.MRD == "Positive" & circ_data$PostRecurrenceSurgery == "TRUE") / sum(circ_data$ctDNA.MRD == "Positive")* 100
positive_ci <- binconf(sum(circ_data$ctDNA.MRD == "Positive" & circ_data$PostRecurrenceSurgery == "TRUE"),
sum(circ_data$ctDNA.MRD == "Positive"),
alpha = 0.05)[c(2, 3)] * 100
negative_rate <- sum(circ_data$ctDNA.MRD == "Negative" & circ_data$PostRecurrenceSurgery == "TRUE") / sum(circ_data$ctDNA.MRD == "Negative")* 100
negative_ci <- binconf(sum(circ_data$ctDNA.MRD == "Negative" & circ_data$PostRecurrenceSurgery == "TRUE"),
sum(circ_data$ctDNA.MRD == "Negative"),
alpha = 0.05)[c(2, 3)] * 100
data <- data.frame(
ctDNA.MRD = c("Positive", "Negative"),
percentage = c(positive_rate, negative_rate),
lower_ci = c(positive_ci[1], negative_ci[1]),
upper_ci = c(positive_ci[2], negative_ci[2])
)
cross_tab <- table(circ_data$ctDNA.MRD, circ_data$PostRecurrenceSurgery)
chi_test <- chisq.test(cross_tab)
p_value <- format.pval(chi_test$p.value, digits = 3)
print(data)print(cross_tab)
FALSE TRUE
Negative 129 90
Positive 185 79print(chi_test)
Pearson's Chi-squared test with Yates' continuity correction
data: cross_tab
X-squared = 6.0858, df = 1, p-value = 0.01363barplot <- ggplot(data, aes(x = ctDNA.MRD, y = percentage, fill = ctDNA.MRD)) +
geom_bar(stat = "identity") +
geom_errorbar(aes(ymin = lower_ci, ymax = upper_ci), width = 0.2) +
geom_text(aes(label = paste0(round(percentage, 1), "%")), vjust = -0.5) +
labs(
x = "ctDNA status at the MRD status",
y = "Proportion of patients undergoing
post-recurrence curative surgery",
caption = paste("Chi-squared test p-value: ", p_value)
) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 50)) +
scale_fill_manual(values = c("Negative" = "blue", "Positive" = "red")) +
theme_minimal()
print(barplot)
#PRS by ctDNA at the MRD Window - pts with Radiological Recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
survfit(Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PRS.months, event = circ_data$OS.Event) ~
ctDNA.MRD, data = circ_data)
18 observations deleted due to missingness
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 219 22 NA 36.3 NA
ctDNA.MRD=POSITIVE 263 52 38.2 29.2 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PRS - Radiological Recurrence | ctDNA MRD window", ylab= "Post-Recurrence Survival", xlab="Time from Radiological Recurrence (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
18 observations deleted due to missingness
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 38.0000 21.0000 0.8073 0.0412 0.7105 0.8745
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 49.0000 45.0000 0.6809 0.0435 0.5872 0.7577 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 482, number of events= 74
(18 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 0.6772 1.9683 0.2546 2.66 0.00782 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 1.968 0.5081 1.195 3.242
Concordance= 0.579 (se = 0.03 )
Likelihood ratio test= 7.63 on 1 df, p=0.006
Wald test = 7.08 on 1 df, p=0.008
Score (logrank) test = 7.35 on 1 df, p=0.007cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 1.97 (1.2-3.24); p = 0.008"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 7.9661, df = 1, p-value = 0.004766fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.00342
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.260726 3.958921
sample estimates:
odds ratio
2.203332 print(contingency_table)
Alive Deceased
Negative 197 22
Positive 211 52table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#PRS by ctDNA at the Surveillance Window - pts with Radiological Recurrence
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
survfit(Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)Call: survfit(formula = Surv(time = circ_data$PRS.months, event = circ_data$OS.Event) ~
ctDNA.Surveillance, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.Surveillance=NEGATIVE 78 2 NA NA NA
ctDNA.Surveillance=POSITIVE 264 41 38.2 36.3 NAevent_summary <- circ_data %>%
group_by(ctDNA.Surveillance) %>%
summarise(
Total = n(),
Events = sum(OS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PRS - Radiological Recurrence | ctDNA Surveillance window", ylab= "Post-Recurrence Survival", xlab="Time from Radiological Recurrence (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))Call: survfit(formula = surv_object ~ ctDNA.Surveillance, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.Surveillance=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 11.0000 2.0000 0.9317 0.0511 0.7237 0.9847
ctDNA.Surveillance=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 41.000 38.000 0.700 0.045 0.602 0.778 circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.Surveillance, data = circ_data)
n= 342, number of events= 43
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.SurveillancePOSITIVE 1.8831 6.5739 0.7248 2.598 0.00938 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.SurveillancePOSITIVE 6.574 0.1521 1.588 27.21
Concordance= 0.606 (se = 0.02 )
Likelihood ratio test= 12.21 on 1 df, p=5e-04
Wald test = 6.75 on 1 df, p=0.009
Score (logrank) test = 8.99 on 1 df, p=0.003cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 6.57 (1.59-27.21); p = 0.009"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 8.0672, df = 1, p-value = 0.004507fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.001475
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
1.735104 60.847334
sample estimates:
odds ratio
6.962255 print(contingency_table)
Alive Deceased
Negative 76 2
Positive 223 41table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the Surveillance Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Vital Status",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Detection ctDNA rates based on sites of relapse
# Remove existing objects and set the working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
# Create a table of counts for the "Rec.Site" variable
relsite_counts <- table(circ_data$Rec.Site)
relsite_df <- as.data.frame(relsite_counts)
names(relsite_df) <- c("RelSite", "Count")
circ_data_pos_mrd <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data_pos_anytime <- circ_data[circ_data$ctDNA.anytime=="POSITIVE",]
pos_counts_mrd <- table(circ_data_pos_mrd$Rec.Site)
pos_counts_anytime <- table(circ_data_pos_anytime$Rec.Site)
relsite_df$MRDPos_Count <- ifelse(is.na(match(relsite_df$RelSite, names(pos_counts_mrd))), 0, pos_counts_mrd[match(relsite_df$RelSite, names(pos_counts_mrd))])
relsite_df$MRDPos_Count[is.na(relsite_df$MRDPos_Count)] <- 0
relsite_df$AnytimePos_Count <- ifelse(is.na(match(relsite_df$RelSite, names(pos_counts_anytime))), 0, pos_counts_anytime[match(relsite_df$RelSite, names(pos_counts_anytime))])
relsite_df$AnytimePos_Count[is.na(relsite_df$AnytimePos_Count)] <- 0
relsite_df$Percent <- (relsite_df$Count / sum(relsite_df$Count)) * 100
relsite_df$MRDPos_Percent <- (relsite_df$MRDPos_Count / relsite_df$Count) * 100
relsite_df$AnytimePos_Percent <- (relsite_df$AnytimePos_Count / relsite_df$Count) * 100
total_observations <- sum(relsite_df$Count)
total_pos_mrd <- sum(relsite_df$MRDPos_Count)
total_pos_anytime <- sum(relsite_df$AnytimePos_Count)
total_row <- data.frame(RelSite = "Total", Count = total_observations, MRDPos_Count = total_pos_mrd, AnytimePos_Count = total_pos_anytime, Percent = 100, MRDPos_Percent = (total_pos_mrd / total_observations) * 100, AnytimePos_Percent = (total_pos_anytime / total_observations) * 100)
relsite_df <- rbind(relsite_df, total_row)
print(relsite_df)
ft <- flextable(relsite_df)
doc <- read_docx() %>%
body_add_flextable(value = ft)
print(doc, target = "relsite_df.docx")#Heatmap for Biomarkers factors
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data %>% arrange(RAS.BRAF)
circ_data$RAS <- factor(circ_data$RAS.BRAF, levels = c("TRUE", "FALSE"))
circ_datadf <- as.data.frame(circ_data)
ha <- HeatmapAnnotation(
RAS.BRAF = circ_data$RAS.BRAF,
TMB = circ_data$TMB,
MSI = circ_data$MSI,
BRAF.V600E = circ_data$BRAF.V600E,
KRAS.G12C = circ_data$KRAS.G12C,
ERBB2 = circ_data$ERBB2,
TP53.Y220C = circ_data$TP53.Y220C,
NTRK = circ_data$NTRK,
RET = circ_data$RET,
col = list(RAS.BRAF = c("TRUE" = "blue","FALSE" = "grey"),
TMB = c("TMB-High" = "blue" , "TMB-Low" = "grey"),
MSI = c("MSI-High" = "blue" , "MSS" = "grey"),
BRAF.V600E = c("MUT" = "blue", "WT" = "grey"),
KRAS.G12C = c("MUT" = "blue", "WT" = "grey"),
ERBB2 = c("MUT" = "blue", "WT" = "grey"),
TP53.Y220C = c("MUT" = "blue", "WT" = "grey"),
NTRK = c("MUT" = "blue", "WT" = "grey"),
RET = c("MUT" = "blue", "WT" = "grey")))
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$RAS.BRAF)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()#Calculate the % altered variables
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
conditions <- list(
RAS.BRAF = "TRUE",
TMB = "TMB-High",
MSI = "MSI-High",
BRAF.V600E = "MUT",
KRAS.G12C = "MUT",
ERBB2 = "MUT",
TP53.Y220C = "MUT",
NTRK = "MUT",
RET = "MUT"
)
total_observations <- nrow(circ_data)
condition_counts <- list()
for (var in names(conditions)) {
condition_value <- conditions[[var]]
condition_count <- sum(circ_data[[var]] == condition_value, na.rm = TRUE)
condition_percentage <- (condition_count / total_observations) * 100
condition_counts[[var]] <- list('Count' = condition_count, 'Percentage' = condition_percentage)
}
condition_counts_df <- do.call(rbind, lapply(names(condition_counts), function(x) {
data.frame(Variable = x,
Count = condition_counts[[x]]$Count,
Percentage = condition_counts[[x]]$Percentage)
}))
print(condition_counts_df)#DFS by Biomarkers
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data %>%
mutate(
RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
)
circ_data_long <- circ_data %>%
gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))
survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)Call: survfit(formula = Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event) ~
value, data = circ_data_long)
n events median 0.95LCL 0.95UCL
value=RAS/BRAF WT 1125 233 NA NA NA
value=TMB High 230 10 NA NA NA
value=MSI High 215 8 NA NA NA
value=BRAF V600E 178 25 NA NA NA
value=KRAS G12C 49 19 33.7 22.1 NA
value=ERBB2 36 12 NA 23.2 NA
value=TP53 Y220C 24 6 NA NA NAevent_summary <- circ_data_long %>%
group_by(value) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)Call:
coxph(formula = surv_obj ~ value, data = circ_data_long)
n= 1857, number of events= 313
coef exp(coef) se(coef) z Pr(>|z|)
valueTMB High -1.6745 0.1874 0.3230 -5.184 2.17e-07 ***
valueMSI High -1.8298 0.1605 0.3596 -5.088 3.62e-07 ***
valueBRAF V600E -0.4366 0.6462 0.2105 -2.074 0.03806 *
valueKRAS G12C 0.7798 2.1810 0.2387 3.267 0.00109 **
valueERBB2 0.5571 1.7456 0.2961 1.882 0.05987 .
valueTP53 Y220C 0.2368 1.2671 0.4135 0.573 0.56693
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
valueTMB High 0.1874 5.3362 0.09950 0.3529
valueMSI High 0.1605 6.2324 0.07929 0.3247
valueBRAF V600E 0.6462 1.5474 0.42779 0.9762
valueKRAS G12C 2.1810 0.4585 1.36608 3.4821
valueERBB2 1.7456 0.5729 0.97711 3.1185
valueTP53 Y220C 1.2671 0.7892 0.56344 2.8497
Concordance= 0.635 (se = 0.012 )
Likelihood ratio test= 107 on 6 df, p=<2e-16
Wald test = 73.9 on 6 df, p=6e-14
Score (logrank) test = 93.74 on 6 df, p=<2e-16KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
KM_curve,
data = circ_data_long,
risk.table = TRUE,
pval = FALSE,
conf.int = FALSE,
break.time.by = 6,
xlab = "Time from surgery (months)",
ylab = "Disease-free Survival",
legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))Call: survfit(formula = surv_obj ~ value, data = circ_data_long)
value=RAS/BRAF WT
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 366.0000 224.0000 0.7755 0.0137 0.7491 0.8028
value=TMB High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 101.0000 10.0000 0.9471 0.0169 0.9146 0.9807
value=MSI High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 96.0000 8.0000 0.9558 0.0159 0.9252 0.9874
value=BRAF V600E
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 69.0000 25.0000 0.8382 0.0311 0.7793 0.9015
value=KRAS G12C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 9.0000 18.0000 0.6023 0.0765 0.4696 0.7726
value=ERBB2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 13.0000 12.0000 0.6287 0.0887 0.4769 0.8289
value=TP53 Y220C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 6.0000 6.0000 0.7237 0.0993 0.5530 0.9470 circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 89.99, df = 6, p-value < 2.2e-16print(contingency_table)
No Recurrence Recurrence
RAS/BRAF WT 892 233
TMB High 220 10
MSI High 207 8
BRAF V600E 153 25
KRAS G12C 30 19
ERBB2 24 12
TP53 Y220C 18 6table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Biomarkers",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 3.044838187274366278739634307811811175382238033645249108971597706840839236974716e-17"#DFS by Biomarkers - Stage I-III
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data <- circ_data %>%
mutate(
RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
)
circ_data_long <- circ_data %>%
gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))
survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)Call: survfit(formula = Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event) ~
value, data = circ_data_long)
n events median 0.95LCL 0.95UCL
value=RAS/BRAF WT 919 121 NA NA NA
value=TMB High 226 9 NA NA NA
value=MSI High 211 7 NA NA NA
value=BRAF V600E 167 19 NA NA NA
value=KRAS G12C 34 8 NA 33.7 NA
value=ERBB2 24 6 NA NA NA
value=TP53 Y220C 19 3 NA NA NAevent_summary <- circ_data_long %>%
group_by(value) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)Call:
coxph(formula = surv_obj ~ value, data = circ_data_long)
n= 1600, number of events= 173
coef exp(coef) se(coef) z Pr(>|z|)
valueTMB High -1.2824 0.2774 0.3456 -3.711 0.000207 ***
valueMSI High -1.4652 0.2310 0.3888 -3.768 0.000164 ***
valueBRAF V600E -0.1646 0.8482 0.2468 -0.667 0.504805
valueKRAS G12C 0.6159 1.8514 0.3655 1.685 0.091909 .
valueERBB2 0.6998 2.0133 0.4183 1.673 0.094374 .
valueTP53 Y220C 0.2591 1.2958 0.5845 0.443 0.657578
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
valueTMB High 0.2774 3.6052 0.1409 0.5461
valueMSI High 0.2310 4.3284 0.1078 0.4950
valueBRAF V600E 0.8482 1.1789 0.5229 1.3759
valueKRAS G12C 1.8514 0.5401 0.9045 3.7894
valueERBB2 2.0133 0.4967 0.8868 4.5707
valueTP53 Y220C 1.2958 0.7717 0.4121 4.0746
Concordance= 0.615 (se = 0.017 )
Likelihood ratio test= 46.29 on 6 df, p=3e-08
Wald test = 35.17 on 6 df, p=4e-06
Score (logrank) test = 41.44 on 6 df, p=2e-07KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
KM_curve,
data = circ_data_long,
risk.table = TRUE,
pval = FALSE,
conf.int = FALSE,
break.time.by = 6,
xlab = "Time from surgery (months)",
ylab = "Disease-free Survival",
legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))Call: survfit(formula = surv_obj ~ value, data = circ_data_long)
value=RAS/BRAF WT
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 320.0000 115.0000 0.8519 0.0132 0.8264 0.8783
value=TMB High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 99.0000 9.0000 0.9505 0.0166 0.9185 0.9837
value=MSI High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 94.0000 7.0000 0.9597 0.0155 0.9298 0.9906
value=BRAF V600E
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 65.0000 19.0000 0.8700 0.0289 0.8150 0.9286
value=KRAS G12C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 7.0000 7.0000 0.7511 0.0863 0.5996 0.9408
value=ERBB2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 9.000 6.000 0.688 0.111 0.502 0.944
value=TP53 Y220C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 5.0000 3.0000 0.8388 0.0854 0.6871 1.0000 circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 39.76, df = 6, p-value = 5.079e-07print(contingency_table)
No Recurrence Recurrence
RAS/BRAF WT 798 121
TMB High 217 9
MSI High 204 7
BRAF V600E 148 19
KRAS G12C 26 8
ERBB2 18 6
TP53 Y220C 16 3table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Biomarkers",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by Biomarkers - Stage IV
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data %>%
mutate(
RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
)
circ_data_long <- circ_data %>%
gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))
survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)Call: survfit(formula = Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event) ~
value, data = circ_data_long)
n events median 0.95LCL 0.95UCL
value=RAS/BRAF WT 206 112 16.95 14.49 35.1
value=TMB High 4 1 NA 1.38 NA
value=MSI High 4 1 NA 1.38 NA
value=BRAF V600E 11 6 23.95 8.71 NA
value=KRAS G12C 15 11 5.62 4.21 NA
value=ERBB2 12 6 8.28 6.31 NA
value=TP53 Y220C 5 3 19.12 6.11 NAevent_summary <- circ_data_long %>%
group_by(value) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)Call:
coxph(formula = surv_obj ~ value, data = circ_data_long)
n= 257, number of events= 140
coef exp(coef) se(coef) z Pr(>|z|)
valueTMB High -0.81504 0.44262 1.00462 -0.811 0.4172
valueMSI High -0.81504 0.44262 1.00462 -0.811 0.4172
valueBRAF V600E -0.10339 0.90177 0.41918 -0.247 0.8052
valueKRAS G12C 0.67999 1.97385 0.31697 2.145 0.0319 *
valueERBB2 -0.05252 0.94883 0.41916 -0.125 0.9003
valueTP53 Y220C 0.01708 1.01723 0.58519 0.029 0.9767
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
valueTMB High 0.4426 2.2593 0.06179 3.171
valueMSI High 0.4426 2.2593 0.06179 3.171
valueBRAF V600E 0.9018 1.1089 0.39654 2.051
valueKRAS G12C 1.9739 0.5066 1.06049 3.674
valueERBB2 0.9488 1.0539 0.41725 2.158
valueTP53 Y220C 1.0172 0.9831 0.32308 3.203
Concordance= 0.53 (se = 0.018 )
Likelihood ratio test= 5.91 on 6 df, p=0.4
Wald test = 6.28 on 6 df, p=0.4
Score (logrank) test = 6.6 on 6 df, p=0.4KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
KM_curve,
data = circ_data_long,
risk.table = TRUE,
pval = FALSE,
conf.int = FALSE,
break.time.by = 6,
xlab = "Time from surgery (months)",
ylab = "Disease-free Survival",
legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))Call: survfit(formula = surv_obj ~ value, data = circ_data_long)
value=RAS/BRAF WT
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 46.0000 109.0000 0.4441 0.0366 0.3779 0.5220
value=TMB High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 2.000 1.000 0.750 0.217 0.426 1.000
value=MSI High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 2.000 1.000 0.750 0.217 0.426 1.000
value=BRAF V600E
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 4.000 6.000 0.436 0.155 0.218 0.874
value=KRAS G12C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 2.000 11.000 0.267 0.114 0.115 0.617
value=ERBB2
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.000 4.000 6.000 0.500 0.144 0.284 0.880
value=TP53 Y220C
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24.0000 1.0000 3.0000 0.3000 0.2387 0.0631 1.0000 circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 5.113, df = 6, p-value = 0.5294print(contingency_table)
No Recurrence Recurrence
RAS/BRAF WT 94 112
TMB High 3 1
MSI High 3 1
BRAF V600E 5 6
KRAS G12C 4 11
ERBB2 6 6
TP53 Y220C 2 3table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "Biomarkers",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by MSI status - All stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~MSI, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
MSI, data = circ_data)
n events median 0.95LCL 0.95UCL
MSI=MSS 2025 506 NA NA NA
MSI=MSI-High 215 8 NA NA NAevent_summary <- circ_data %>%
group_by(MSI) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ MSI, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - MSI Status | All stages", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("MSS", "MSI-High"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ MSI, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
MSI=MSS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 624 490 0.730 0.0108 0.709 0.751
30 386 9 0.717 0.0114 0.694 0.739
36 183 6 0.703 0.0126 0.678 0.727
MSI=MSI-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 96 8 0.956 0.0159 0.911 0.978
30 61 0 0.956 0.0159 0.911 0.978
36 26 0 0.956 0.0159 0.911 0.978circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
cox_fit <- coxph(surv_object ~ MSI, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ MSI, data = circ_data)
n= 2240, number of events= 514
coef exp(coef) se(coef) z Pr(>|z|)
MSIMSI-High -2.0456 0.1293 0.3564 -5.74 9.47e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
MSIMSI-High 0.1293 7.734 0.06431 0.26
Concordance= 0.546 (se = 0.004 )
Likelihood ratio test= 69.67 on 1 df, p=<2e-16
Wald test = 32.95 on 1 df, p=9e-09
Score (logrank) test = 46.16 on 1 df, p=1e-11cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.13 (0.06-0.26); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 48.522, df = 1, p-value = 3.266e-12fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 5.283e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.04912506 0.23532131
sample estimates:
odds ratio
0.1160797 print(contingency_table)
No Recurrence Recurrence
MSS 1519 506
MSI-High 207 8table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "MSI Status",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by TMB status - All stages
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$TMB <- factor(circ_data$TMB, levels = c("TMB-Low", "TMB-High"), labels = c("TMB-Low", "TMB-High"))
survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~MSI, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) ~
MSI, data = circ_data)
n events median 0.95LCL 0.95UCL
MSI=MSI-High 215 8 NA NA NA
MSI=MSS 2025 506 NA NA NAevent_summary <- circ_data %>%
group_by(TMB) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ TMB, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - TMB Status | All stages", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("TMB-Low", "TMB-High"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))Call: survfit(formula = surv_object ~ TMB, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
TMB=TMB-Low
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 619 488 0.730 0.0108 0.708 0.750
30 381 9 0.717 0.0115 0.693 0.738
36 179 6 0.702 0.0127 0.676 0.726
TMB=TMB-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
24 101 10 0.947 0.0169 0.902 0.972
30 66 0 0.947 0.0169 0.902 0.972
36 30 0 0.947 0.0169 0.902 0.972circ_data$TMB <- factor(circ_data$TMB, levels = c("TMB-Low", "TMB-High"), labels = c("TMB-Low", "TMB-High"))
cox_fit <- coxph(surv_object ~ TMB, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ TMB, data = circ_data)
n= 2240, number of events= 514
coef exp(coef) se(coef) z Pr(>|z|)
TMBTMB-High -1.8953 0.1503 0.3194 -5.934 2.96e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TMBTMB-High 0.1503 6.654 0.08036 0.281
Concordance= 0.548 (se = 0.004 )
Likelihood ratio test= 69.32 on 1 df, p=<2e-16
Wald test = 35.21 on 1 df, p=3e-09
Score (logrank) test = 47.09 on 1 df, p=7e-12cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 0.15 (0.08-0.28); p = 0"circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$TMB, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 48.98, df = 1, p-value = 2.586e-12fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 6.3e-16
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.06377761 0.25743811
sample estimates:
odds ratio
0.1358906 print(contingency_table)
No Recurrence Recurrence
TMB-Low 1506 504
TMB-High 220 10table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "TMB Status",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Percentage of ctDNA MRD Window positivity in biomarker groups
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data %>%
mutate(
RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
)
circ_data_long <- circ_data %>%
gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
filter(!is.na(value))
summary_data <- circ_data_long %>%
group_by(value) %>%
summarise(
n = n(),
positive = sum(ctDNA.MRD == "POSITIVE"),
pct_positive = (positive / n) * 100,
se = sqrt((pct_positive / 100) * (1 - pct_positive / 100) / n),
ci_low = pct_positive - 1.96 * se * 100,
ci_high = pct_positive + 1.96 * se * 100
)
overall_summary <- circ_data_long %>%
summarise(
value = "Overall",
n = n(),
positive = sum(ctDNA.MRD == "POSITIVE"),
pct_positive = (positive / n) * 100,
se = sqrt((pct_positive / 100) * (1 - pct_positive / 100) / n),
ci_low = pct_positive - 1.96 * se * 100,
ci_high = pct_positive + 1.96 * se * 100
)
summary_data <- bind_rows(overall_summary, summary_data)
summary_data$value <- factor(summary_data$value, levels = c("Overall", "RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))
ggplot(summary_data, aes(x = value, y = pct_positive)) +
geom_bar(stat = "identity", fill = "blue", alpha = 0.7) +
geom_errorbar(aes(ymin = ci_low, ymax = ci_high), width = 0.2) +
geom_text(aes(label = sprintf("%.1f%%", pct_positive)), vjust = -0.5, color = "black") +
labs(
x = "Genetic Mutation",
y = "Post-surgical MRD positivity %"
) +
theme(
panel.background = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(color = "black"),
axis.ticks = element_line(color = "black"),
axis.text.x = element_text(angle = 45, hjust = 1),
plot.background = element_blank())
#DFS by ctDNA at the MRD Window - BRAF V600E Landmark MRD timepoint
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$BRAF.V600E=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
ctDNA.MRD, data = circ_data)
n events median 0.95LCL 0.95UCL
ctDNA.MRD=NEGATIVE 152 12 NA NA NA
ctDNA.MRD=POSITIVE 11 11 2.89 1.38 NAevent_summary <- circ_data %>%
group_by(ctDNA.MRD) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | BRAF V600E", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))Call: survfit(formula = surv_object ~ ctDNA.MRD, data = circ_data,
conf.int = 0.95, conf.type = "log-log")
ctDNA.MRD=NEGATIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 152 0 1.000 0.0000 1.000 1.000
24 65 12 0.897 0.0296 0.821 0.942
ctDNA.MRD=POSITIVE
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 11 0 1 0 1 1 circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 163, number of events= 23
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 5.5020 245.1912 0.8061 6.826 8.75e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 245.2 0.004078 50.51 1190
Concordance= 0.764 (se = 0.049 )
Likelihood ratio test= 67.61 on 1 df, p=<2e-16
Wald test = 46.59 on 1 df, p=9e-12
Score (logrank) test = 265.5 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)[1] "HR = 245.19 (50.51-1190.25); p = 0"rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$BRAF.V600E=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test with Yates' continuity correction
data: contingency_table
X-squared = 64.403, df = 1, p-value = 1.014e-15fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 3.531e-11
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
24.33026 Inf
sample estimates:
odds ratio
Inf print(contingency_table)
No Recurrence Recurrence
Negative 140 12
Positive 0 11table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value with high precision: 1.013866302450570686113374440835884895795686441374860997655105165904387831687927e-15"#DFS by ctDNA at the MRD Window - Forest plot with all subgroups of biomarkers
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$DFS.MRD.months >= 0,]
perform_cox <- function(data, filter_col = NULL, filter_val = NULL) {
if (!is.null(filter_col) & !is.null(filter_val)) {
data <- data[data[[filter_col]] == filter_val,]
}
surv_object <- Surv(time = data$DFS.MRD.months, event = data$DFS.Event)
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = data)
cox_fit_summary <- summary(cox_fit)
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
return(c(HR, lower_CI, upper_CI, p_value))
}
results <- data.frame(
Subgroup = c("All", "RAS/BRAF WT", "TMB-High", "MSI-High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
HR = rep(NA, 8),
lower_CI = rep(NA, 8),
upper_CI = rep(NA, 8),
p_value = rep(NA, 8)
)
results[1, 2:5] <- perform_cox(circ_data)
results[2, 2:5] <- perform_cox(circ_data, "RAS.BRAF", "TRUE")
results[3, 2:5] <- perform_cox(circ_data, "TMB", "TMB-High")
results[4, 2:5] <- perform_cox(circ_data, "MSI", "MSI-High")
results[5, 2:5] <- perform_cox(circ_data, "BRAF.V600E", "MUT")
results[6, 2:5] <- perform_cox(circ_data, "KRAS.G12C", "MUT")
results[7, 2:5] <- perform_cox(circ_data, "ERBB2", "MUT")
results[8, 2:5] <- perform_cox(circ_data, "TP53.Y220C", "MUT")
results$HR <- as.numeric(results$HR)
results$lower_CI <- as.numeric(results$lower_CI)
results$upper_CI <- as.numeric(results$upper_CI)
results$p_value <- as.numeric(results$p_value)
results$label_text <- paste0(
"HR = ", round(results$HR, 2),
"\n95% CI = ", round(results$lower_CI, 2), "-", round(results$upper_CI, 2),
"\np = ", round(results$p_value, 3)
)
ggplot(results, aes(x = Subgroup, y = HR)) +
geom_point(size = 3) +
geom_errorbar(aes(ymin = lower_CI, ymax = upper_CI), width = 0.2) +
geom_text(aes(label = label_text), hjust = -0.2, vjust = 0.5, size = 3.5) +
scale_y_log10() +
geom_hline(yintercept = 1, linetype = "dashed") +
labs(title = "Forest Plot of HR for DFS between ctDNA Positive versus Negative",
x = "Subgroup",
y = "Hazard Ratio (HR)") +
coord_flip() +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
#DFS by ctDNA at the MRD Window - individual cox regression models for each biomarker to extract the exact p value
#ERBB2 Amplification
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ERBB2=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 33, number of events= 12
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.8717 17.6668 0.6922 4.148 3.35e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 17.67 0.0566 4.549 68.61
Concordance= 0.808 (se = 0.046 )
Likelihood ratio test= 20.56 on 1 df, p=6e-06
Wald test = 17.21 on 1 df, p=3e-05
Score (logrank) test = 29.02 on 1 df, p=7e-08cox_fit_summary <- summary(cox_fit)
#KRAS G12C Amplification
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$KRAS.G12C=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 47, number of events= 18
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.3978 10.9994 0.4904 4.889 1.01e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 11 0.09091 4.206 28.76
Concordance= 0.746 (se = 0.048 )
Likelihood ratio test= 21.56 on 1 df, p=3e-06
Wald test = 23.9 on 1 df, p=1e-06
Score (logrank) test = 35.4 on 1 df, p=3e-09cox_fit_summary <- summary(cox_fit)
#MSI-High
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 202, number of events= 7
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 4.2671 71.3153 0.7729 5.521 3.37e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 71.32 0.01402 15.68 324.4
Concordance= 0.822 (se = 0.086 )
Likelihood ratio test= 22.32 on 1 df, p=2e-06
Wald test = 30.48 on 1 df, p=3e-08
Score (logrank) test = 112.6 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#TMB-High
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$TMB=="TMB-High",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 217, number of events= 9
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 3.5631 35.2728 0.6756 5.274 1.33e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 35.27 0.02835 9.384 132.6
Concordance= 0.755 (se = 0.083 )
Likelihood ratio test= 18.23 on 1 df, p=2e-05
Wald test = 27.82 on 1 df, p=1e-07
Score (logrank) test = 72.18 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#RAS/BRAF WT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RAS.BRAF=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data)
ggforest(cox_fit,data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ ctDNA.MRD, data = circ_data)
n= 1057, number of events= 224
coef exp(coef) se(coef) z Pr(>|z|)
ctDNA.MRDPOSITIVE 2.4770 11.9052 0.1361 18.2 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
ctDNA.MRDPOSITIVE 11.91 0.084 9.118 15.54
Concordance= 0.742 (se = 0.015 )
Likelihood ratio test= 291 on 1 df, p=<2e-16
Wald test = 331.4 on 1 df, p=<2e-16
Score (logrank) test = 527.1 on 1 df, p=<2e-16cox_fit_summary <- summary(cox_fit)
#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))[1] "Exact p-value for ctDNA.MRD: 4.748165471590925655037416254167659684199577000367067505486032167898691773378702e-74"#DFS by BRAF & MSI - ctDNA Positive Landmark MRD timepoint
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$ctDNA.MRD == "POSITIVE",]
circ_data <- circ_data[circ_data$DFS.MRD.months >= 0,]
# Create the BRAF.MSI variable
circ_data$BRAF.MSI <- NA
circ_data <- circ_data %>%
mutate(BRAF.MSI = case_when(
BRAF.V600E == "WT" & MSI == "MSS" ~ 1,
BRAF.V600E == "WT" & MSI == "MSI-High" ~ 2,
BRAF.V600E == "MUT" & MSI == "MSI-High" ~ 3,
BRAF.V600E == "MUT" & MSI == "MSS" ~ 4
))
circ_data$BRAF.MSI <- factor(circ_data$BRAF.MSI, levels = c(1, 2, 3, 4),
labels = c("BRAF WT & MSS", "BRAF WT & MSI-High",
"BRAF V600E & MSI-High", "BRAF V600E & MSS"))
print(table(circ_data$BRAF.MSI, useNA = "ifany"))
BRAF WT & MSS BRAF WT & MSI-High BRAF V600E & MSI-High BRAF V600E & MSS <NA>
320 5 1 10 1 circ_data <- circ_data[!is.na(circ_data$BRAF.MSI),]
if(nrow(circ_data) == 0) {
stop("No non-missing observations in the dataset after filtering.")
}
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~BRAF.MSI, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
BRAF.MSI, data = circ_data)
n events median 0.95LCL 0.95UCL
BRAF.MSI=BRAF WT & MSS 320 249 5.520 4.895 7.16
BRAF.MSI=BRAF WT & MSI-High 5 3 4.731 0.559 NA
BRAF.MSI=BRAF V600E & MSI-High 1 1 0.624 NA NA
BRAF.MSI=BRAF V600E & MSS 10 10 3.285 1.380 NAevent_summary <- circ_data %>%
group_by(BRAF.MSI) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95, conf.type = "log-log")
# Plot the Kaplan-Meier curve
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 6, palette = c("blue", "green", "purple", "red"),
title = "DFS - BRAF & MSI | ctDNA MRD Positive", ylab = "Disease-Free Survival",
xlab = "Time from Landmark Time point (Months)",
legend.labs = c("BRAF WT & MSS", "BRAF WT & MSI-High",
"BRAF V600E & MSI-High", "BRAF V600E & MSS"),
legend.title = "")
summary(KM_curve, times = c(0, 24))Call: survfit(formula = surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
BRAF.MSI=BRAF WT & MSS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 320 4 0.988 0.00621 0.967 0.995
24 34 240 0.209 0.02448 0.163 0.259
BRAF.MSI=BRAF WT & MSI-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 5 0 1.0 0.000 1.000 1.000
24 2 3 0.4 0.219 0.052 0.753
BRAF.MSI=BRAF V600E & MSI-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 1 0 1 0 1 1
BRAF.MSI=BRAF V600E & MSS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 10 0 1 0 1 1 cox_fit <- coxph(surv_object ~ BRAF.MSI, data = circ_data)
summary(cox_fit)Call:
coxph(formula = surv_object ~ BRAF.MSI, data = circ_data)
n= 336, number of events= 263
coef exp(coef) se(coef) z Pr(>|z|)
BRAF.MSIBRAF WT & MSI-High -0.2883 0.7495 0.5818 -0.496 0.62018
BRAF.MSIBRAF V600E & MSI-High 2.6324 13.9073 1.0209 2.579 0.00992 **
BRAF.MSIBRAF V600E & MSS 0.7860 2.1947 0.3250 2.419 0.01557 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
BRAF.MSIBRAF WT & MSI-High 0.7495 1.3342 0.2397 2.344
BRAF.MSIBRAF V600E & MSI-High 13.9073 0.0719 1.8805 102.851
BRAF.MSIBRAF V600E & MSS 2.1947 0.4556 1.1608 4.149
Concordance= 0.511 (se = 0.008 )
Likelihood ratio test= 8.29 on 3 df, p=0.04
Wald test = 12.54 on 3 df, p=0.006
Score (logrank) test = 17.48 on 3 df, p=6e-04circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$BRAF.MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)Warning in stats::chisq.test(x, y, ...) :
Chi-squared approximation may be incorrectprint(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 4.0751, df = 3, p-value = 0.2535fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 0.232
alternative hypothesis: two.sidedprint(contingency_table)
No Recurrence Recurrence
BRAF WT & MSS 71 249
BRAF WT & MSI-High 2 3
BRAF V600E & MSI-High 0 1
BRAF V600E & MSS 0 10table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
#DFS by BRAF & MSI - ctDNA Negative Landmark MRD timepoint
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$ctDNA.MRD == "NEGATIVE",]
circ_data <- circ_data[circ_data$DFS.MRD.months > 0,]
circ_data$BRAF.MSI <- NA
circ_data <- circ_data %>%
mutate(BRAF.MSI = case_when(
BRAF.V600E == "WT" & MSI == "MSS" ~ 1,
BRAF.V600E == "WT" & MSI == "MSI-High" ~ 2,
BRAF.V600E == "MUT" & MSI == "MSI-High" ~ 3,
BRAF.V600E == "MUT" & MSI == "MSS" ~ 4
))
circ_data$BRAF.MSI <- factor(circ_data$BRAF.MSI, levels = c(1, 2, 3, 4),
labels = c("BRAF WT & MSS", "BRAF WT & MSI-High",
"BRAF V600E & MSI-High", "BRAF V600E & MSS"))
print(table(circ_data$BRAF.MSI, useNA = "ifany"))
BRAF WT & MSS BRAF WT & MSI-High BRAF V600E & MSI-High BRAF V600E & MSS
1526 93 103 49 circ_data <- circ_data[!is.na(circ_data$BRAF.MSI),]
if (any(!is.finite(circ_data$DFS.MRD.months)) || any(!is.finite(circ_data$DFS.Event))) {
stop("Data contains non-finite values.")
}
if (nrow(circ_data) == 0) {
stop("No non-missing observations in the dataset after filtering.")
}
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~BRAF.MSI, data = circ_data)Call: survfit(formula = Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) ~
BRAF.MSI, data = circ_data)
n events median 0.95LCL 0.95UCL
BRAF.MSI=BRAF WT & MSS 1526 219 NA NA NA
BRAF.MSI=BRAF WT & MSI-High 93 0 NA NA NA
BRAF.MSI=BRAF V600E & MSI-High 103 3 NA NA NA
BRAF.MSI=BRAF V600E & MSS 49 9 NA NA NAevent_summary <- circ_data %>%
group_by(BRAF.MSI) %>%
summarise(
Total = n(),
Events = sum(DFS.Event),
Fraction = Events / n(),
Percentage = (Events / n()) * 100
)
print(event_summary)surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95, conf.type = "log-log")
# Plot the Kaplan-Meier curve
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE,
break.time.by = 6, palette = c("blue", "green", "purple", "red"),
title = "DFS - BRAF & MSI | ctDNA MRD Negative", ylab = "Disease-Free Survival",
xlab = "Time from Landmark Time point (Months)",
legend.labs = c("BRAF WT & MSS", "BRAF WT & MSI-High",
"BRAF V600E & MSI-High", "BRAF V600E & MSS"),
legend.title = "")
summary(KM_curve, times = c(0, 24))Call: survfit(formula = surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95,
conf.type = "log-log")
BRAF.MSI=BRAF WT & MSS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 1526 0 1.000 0.0000 1.000 1.000
24 519 210 0.838 0.0106 0.816 0.858
BRAF.MSI=BRAF WT & MSI-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 93 0 1 0 1 1
24 41 0 1 0 NA NA
BRAF.MSI=BRAF V600E & MSI-High
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 103 0 1.000 0.0000 1.000 1.000
24 45 3 0.954 0.0269 0.859 0.985
BRAF.MSI=BRAF V600E & MSS
time n.risk n.event survival std.err lower 95% CI upper 95% CI
0 49 0 1.000 0.0000 1.000 1.000
24 20 9 0.788 0.0658 0.622 0.887cox_fit <- coxphf(surv_object ~ BRAF.MSI, data = circ_data)
summary(cox_fit)coxphf(formula = surv_object ~ BRAF.MSI, data = circ_data)
Model fitted by Penalized ML
Confidence intervals and p-values by Profile Likelihood
coef se(coef) exp(coef) lower 0.95 upper 0.95 Chisq p
BRAF.MSIBRAF WT & MSI-High -3.4234200 1.4219914 0.03260075 0.0002591613 0.2215540 25.2847247 4.946103e-07
BRAF.MSIBRAF V600E & MSI-High -1.5067027 0.5411239 0.22163959 0.0620647658 0.5473653 13.3792192 2.544276e-04
BRAF.MSIBRAF V600E & MSS 0.2475077 0.3328541 1.28082917 0.6222123751 2.3148209 0.5176429 4.718489e-01
Likelihood ratio test=38.29511 on 3 df, p=2.44771e-08, n=1771
Wald test = 14.17091 on 3 df, p = 0.002681504
Covariance-Matrix:
BRAF.MSIBRAF WT & MSI-High BRAF.MSIBRAF V600E & MSI-High BRAF.MSIBRAF V600E & MSS
BRAF.MSIBRAF WT & MSI-High 2.022059448 0.004612586 0.004606403
BRAF.MSIBRAF V600E & MSI-High 0.004612586 0.292815067 0.004594608
BRAF.MSIBRAF V600E & MSS 0.004606403 0.004594608 0.110791854circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$BRAF.MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
Pearson's Chi-squared test
data: contingency_table
X-squared = 26.796, df = 3, p-value = 6.497e-06fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
Fisher's Exact Test for Count Data
data: contingency_table
p-value = 2.589e-08
alternative hypothesis: two.sidedprint(contingency_table)
No Recurrence Recurrence
BRAF WT & MSS 1307 219
BRAF WT & MSI-High 93 0
BRAF V600E & MSI-High 100 3
BRAF V600E & MSS 40 9table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
geom_bar(stat = "identity") +
geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
theme_minimal() +
labs(title = "ctDNA at the MRD Window",
x = "ctDNA",
y = "Patients (%)",
fill = "Recurrence",
caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
scale_y_continuous(labels = scales::percent_format()) +
scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
legend.text = element_text(size = 12, color = "black")) # increase Recurrence label size
---
title: "CIRCULATE Galaxy Nakamura et al 2024"
output: html_notebook
---

library(swimplot)
library(Rmpfr)
library(coxphf)
library(grid)
library(gtable)
library(readr) 
library(mosaic)
library(dplyr) 
library(survival) 
library(survminer)
library(gridtext)
library(ggplot2)
library(scales)
library(officer)
library(ggthemes)
library(tidyverse)
library(gtsummary)
library(flextable)
library(parameters)
library(car)
library(grid)
library(ComplexHeatmap)
library(readxl)
library(janitor)
library(rms)
library(DT)

#Demographics Table
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]

circ_data_subset <- circ_data %>%
  select(
    Age,
    Gender,
    ECOG,
    PrimSite,
    pT,
    pN,
    Stage,
    NAC,
    ACT,
    BRAF.V600E,
    RAS,
    MSI,
    RFS.Event,
    OS.months) %>%
  mutate(
    Age = as.numeric(Age),
    Gender = factor(Gender, levels = c("Male", "Female")),
    ECOG = factor(ECOG, levels = c(0, 1)),
    PrimSite = factor(PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum")),
    pT = factor(pT, levels = c("T1-T2", "T3-T4")),
    pN = factor(pN, levels = c("N0", "N1-N2")),
    Stage = factor(Stage, levels = c("I","II", "III", "IV")),
    NAC = factor(NAC, levels = c("TRUE", "FALSE"), labels = c("Neoadjuvant Chemotherapy", "Upfront Surgery")),
    ACT = factor(ACT, levels = c("TRUE", "FALSE"), labels = c("Adjuvant Chemotherapy", "Observation")),
    BRAF.V600E = factor(BRAF.V600E, levels = c("WT", "MUT"), labels = c("BRAF wt", "BRAF V600E")),
    RAS = factor(RAS, levels = c("WT", "MUT"), labels = c("RAS wt", "RAS mut")),
    MSI = factor(MSI, levels = c("MSS", "MSI-High")),
    RFS.Event = factor(RFS.Event, levels = c("TRUE", "FALSE"), labels = c("Recurrence", "No Recurrence")),
    OS.months = as.numeric(OS.months))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```


#Table of Metastatic organ involvement in Stage IV
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Stage=="IV",]

circ_data_subset <- circ_data %>%
  select(
    Mets.Organ) %>%
  mutate(
    Mets.Organ = factor(Mets.Organ))
table1 <- circ_data_subset %>%
  tbl_summary(
    statistic = list(
      all_continuous() ~ "{median} ({min} - {max})",
      all_categorical() ~ "{n} ({p}%)")) %>%
  bold_labels()
table1
fit1 <- as_flex_table(
  table1,
  include = everything(),
  return_calls = FALSE,
  strip_md_bold = TRUE)
fit1
save_as_docx(fit1, path= "~/Downloads/table1.docx")
```

#ctDNA Detection Rates by Window and Stages
```{r}
#ctDNA at Baseline
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data$ctDNA.Baseline <- factor(circ_data$ctDNA.Baseline, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Baseline %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Baseline, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Baseline == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at MRD Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.MRD == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.MRD, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.MRD == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)

#ctDNA at Surveillance Window
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
circ_data <- subset(circ_data, ctDNA.Surveillance %in% c("NEGATIVE", "POSITIVE"))
circ_data$Stage <- factor(circ_data$Stage, levels=c("I","II", "III","IV"))
positive_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance == "POSITIVE", by=list(circ_data$Stage), FUN=sum)
total_counts_by_stage <- aggregate(circ_data$ctDNA.Surveillance, by=list(circ_data$Stage), FUN=length)
combined_data <- data.frame(
  Stage = total_counts_by_stage$Group.1,
  Total_Count = total_counts_by_stage$x,
  Positive_Count = positive_counts_by_stage$x,
  Rate = (positive_counts_by_stage$x / total_counts_by_stage$x) * 100  # Convert to percentage
)
combined_data$Rate <- sprintf("%.2f%%", combined_data$Rate)
overall_total_count <- nrow(circ_data)
overall_positive_count <- nrow(circ_data[circ_data$ctDNA.Surveillance == "POSITIVE",])
overall_positivity_rate <- (overall_positive_count / overall_total_count) * 100  # Convert to percentage
overall_row <- data.frame(
  Stage = "Overall",
  Total_Count = overall_total_count,
  Positive_Count = overall_positive_count,
  Rate = sprintf("%.2f%%", overall_positivity_rate)
)
combined_data <- rbind(combined_data, overall_row)
print(combined_data)
```



#DFS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 1024)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```




#DFS by ctDNA at the MRD Window - Stage I Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage I", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("II", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage I", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#DFS by ctDNA at the MRD Window - Stage II Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "III", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```

#DFS by ctDNA at the MRD Window - Stage III Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage III", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```






#DFS by ctDNA at the MRD Window - High Risk Stage II Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - High Risk Stage II", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```






#DFS by ctDNA at the MRD Window - High Risk Stage III Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageIII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | High Risk Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$Risk.StageIII==TRUE,]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - High Risk Stage III", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```






#DFS by ctDNA at the MRD Window - Stage I-III Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage I-III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage I-III", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```

#DFS by ctDNA at the MRD Window - Stage IV Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | Stage IV", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage IV", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```










#OS by ctDNA at the MRD Window - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | All stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```




#OS by ctDNA at the MRD Window - Stage I-III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | Stage I-III", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window - Stage I-III", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```










#OS by ctDNA at the MRD Window - Stage IV
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA MRD window | Stage IV", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```










#Multivariate cox regression at MRD Window for DFS - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPositive", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))
```


#Multivariate cox regression at MRD Window for OS - All stages Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.MRD + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for OS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - High Risk Stage II/III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, ECOG and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - High Risk Stage II/III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II/III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI and pathological stage
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$HighRisk.Stage=="TRUE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + Stage + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - High Risk Stage II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))

circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - High Risk Stage II
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | High Risk Stage II", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Risk.StageII==TRUE,]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - Stage II T3N0
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | T3N0", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T3N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))

circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - Stage II T4N0
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | T4N0", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$StageII.Group=="T4N0",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))

circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - Stage III
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage III", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "IV")),]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD negative - Stage IV NAC-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage IV NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD Negative - Stage IV no NAC-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Negative ACT vs Observation | Stage IV No NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - Stage IV NAC-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage IV NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ACT treatment in MRD positive - Stage IV no NAC-treated
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ACT, data = circ_data)
event_summary <- circ_data %>%
  group_by(ACT) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ACT, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - ctDNA MRD Positive ACT vs Observation | Stage IV No NAC-treated", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Observation", "ACT"), legend.title="")
summary(KM_curve, times= c(3, 6, 18, 24))
circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
cox_fit <- coxph(surv_object ~ ACT, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ACT, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ACT vs Observation", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Adjusted HR "ACT vs no ACT" - age, gender, MSI, pathological stage, and performance status
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("TRUE","FALSE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)

#Same analysis; Non ACT as reference
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data[circ_data$NAC=="FALSE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data$DFS.months=circ_data$DFS.months-2
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ACT <- factor(circ_data$ACT, levels=c("FALSE","TRUE"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", "≥70"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Stage <- factor(circ_data$Stage, levels = c("II", "III"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$Colon <- factor(circ_data$PrimSite, levels = c("Right-sided colon", "Left-sided colon", "Rectum"))
circ_data$ECOG <- factor(circ_data$ECOG, levels=c("0","1"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-HIGH"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ACT + Gender + Age.Group + ECOG, data=circ_data)
summary(cox_fit)
```


#DFS by ctDNA Clearance ACT-treated at 3 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$DFS.3mo.months>=0,]
survfit(Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.3mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance at 3 months", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#OS by ctDNA Clearance ACT-treated at 3 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.3months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$OS.3mo.months>=0,]
survfit(Surv(time = circ_data$OS.3mo.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.3mo.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Clearance from MRD to 3 months ACT-treated | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance at 3 months", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#DFS by ctDNA Clearance ACT-treated at 6 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$DFS.6mo.months>=0,]
survfit(Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.6mo.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance at 6 months", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#OS by ctDNA Clearance ACT-treated at 6 months - all stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$ACT==TRUE,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "NEGATIVE" ~ 1,
    ctDNA.MRD == "POSITIVE" & ctDNA.6months == "POSITIVE" ~ 2
  ))

circ_data <- circ_data[circ_data$OS.6mo.months>=0,]
survfit(Surv(time = circ_data$OS.6mo.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.6mo.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Clearance from MRD to 6 months ACT-treated | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("Clearance", "No Clearance"), legend.title="")
summary(KM_curve, times= c(6, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2"), labels = c("Clearance", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA clearance at 6 months", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#Sankey plot for 3 months to 6 months ctDNA clearance
```{r}
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA + MRD window [185] ACT-treated #ADD8E6
#ctDNA + MRD window [151] Not treated #808080
#ACT-treated [100] ctDNA Clearance at 3 months #87EA86
#ACT-treated [71] No Clearance at 3 months #E67272
#ACT-treated [14] No 3 months time point #808080
#ctDNA Clearance at 3 months [7] ctDNA + at 6 months #E67272
#ctDNA Clearance at 3 months [64] ctDNA - at 6 months #87EA86
#ctDNA Clearance at 3 months [29] No 6 months time point #808080
#No Clearance at 3 months [27] ctDNA + at 6 months #E67272
#No Clearance at 3 months [11] ctDNA - at 6 months #87EA86
#No Clearance at 3 months [33] No 6 months time point #808080
#No 3 months time point [2] ctDNA + at 6 months #E67272
#No 3 months time point [2] ctDNA - at 6 months #87EA86
#No 3 months time point [10] No 6 months time point #808080
```

#Number of MRD positive patients & ctDNA clearance on ACT
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

# Count the number of MRD positive patients
number_of_positive_patients <- sum(circ_datadf$ctDNA.MRD == "POSITIVE", na.rm = TRUE)
print(paste("Number of MRD positive patients:", number_of_positive_patients))

# Count the number & percentage of MRD positive patients treated with ACT
positive_subset <- sum(circ_datadf$ACT == "TRUE" & circ_datadf$ctDNA.MRD == "POSITIVE", na.rm = TRUE)
print(paste("Number of MRD positive patients treated with ACT:", positive_subset))
percentage_positive_for_both <- (positive_subset / number_of_positive_patients) * 100
print(paste("Percentage of MRD positive patients treated with ACT:", percentage_positive_for_both, "%"))

# Count the number & percentage of patients with ctDNA clearance post-ACT
clearance_postACT <- sum(
  (circ_datadf$ACT == "TRUE") & 
    (circ_datadf$ctDNA.MRD == "POSITIVE") & 
    (circ_datadf$Clearance.Event == "TRUE"), 
  na.rm = TRUE
)
print(paste("Number of patients with ctDNA Clearance post-ACT:", clearance_postACT))
percentage_clearance <- (clearance_postACT / positive_subset) * 100
print(paste("ctDNA Clearance post-ACT:", percentage_clearance, "%"))

# Count the number of patients with subsequent timepoints available
clearance_subset <- sum(
  (circ_datadf$ACT == "TRUE") & 
    (circ_datadf$ctDNA.MRD == "POSITIVE") & 
    (circ_datadf$Transient.Clearance == "TRUE" | circ_datadf$Transient.Clearance == "FALSE"), 
  na.rm = TRUE
)
print(paste("Number of patients with subsequent timepoints available:", clearance_subset))

# Count the number & percentage of patients with sustained clearance
clearance_sustained <- sum(
  (circ_datadf$ACT == "TRUE") & 
    (circ_datadf$ctDNA.MRD == "POSITIVE") & 
    (circ_datadf$Transient.Clearance == "FALSE"), 
  na.rm = TRUE
)
print(paste("Number of patients with sustained clearance:", clearance_sustained))
percentage_sustained_clearance <- (clearance_sustained / clearance_subset) * 100
print(paste("Sustained ctDNA Clearance:", percentage_sustained_clearance, "%"))

# Count the number & percentage of patients with transient clearance
clearance_transient <- sum(
  (circ_datadf$ACT == "TRUE") & 
    (circ_datadf$ctDNA.MRD == "POSITIVE") & 
    (circ_datadf$Transient.Clearance == "TRUE"), 
  na.rm = TRUE
)
print(paste("Number of patients with transient clearance:", clearance_transient))
percentage_transient_clearance <- (clearance_transient / clearance_subset) * 100
print(paste("Transient ctDNA Clearance:", percentage_transient_clearance, "%"))
```

#Sankey plot for Sustained vs Transient Clearance
```{r}
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA + MRD window [185] ACT-treated #ADD8E6
#ctDNA + MRD window [151] Not treated #808080
#ACT-treated [126] ctDNA post-MRD Clearance #87EA86
#ACT-treated [55] No Clearance #E67272
#ACT-treated [4] No post-MRD time point #808080
#No Clearance [55] No Clearance analysis #E67272
#ctDNA post-MRD Clearance [126] Available post-MRD Timepoints #ADD8E66
#Available post-MRD Timepoints [68] Sustained Clearance #7393B3
#Available post-MRD Timepoints [58] Transient Clearance #87EA86
```

#DFS by ctDNA Clearance post-MRD - 3 Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.Clearance!="",]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.Clearance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Clearance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue","green"), title="DFS - ctDNA Clearance post-MRD | All Stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("No Clearance", "Sustained", "Transient"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))
circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
cox_fit <- coxph(surv_object ~ ctDNA.Clearance, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for No Clearance
z_value <- cox_fit_summary$coefficients["ctDNA.ClearanceNo Clearance", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for No Clearance:", format(p_value_mpfr, scientific = TRUE)))

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Clearance, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA Dynamics post-MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```

#Levels of MRD MTM/mL in Clearance post-MRD log10 transformation
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[!is.na(circ_data$ctDNA.Clearance) & circ_data$ctDNA.Clearance != "",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- as.data.frame(circ_data)

# Transform p_MRD_MTM with log10
circ_data$p_MRD_MTM <- as.numeric(as.character(circ_data$p_MRD_MTM))
circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
median_p_MRD_MTM <- aggregate(p_MRD_MTM ~ ctDNA.Clearance, data = circ_data, FUN = median)
print(median_p_MRD_MTM)

# Create violin plot with log10 scale on y-axis
ggplot(circ_data, aes(x=ctDNA.Clearance, y=p_MRD_MTM, fill=ctDNA.Clearance)) +
  geom_violin(trim=FALSE) +
  scale_fill_manual(values=c("Sustained"="lightblue", "Transient"="lightgreen", "No Clearance"="salmon")) +
  geom_boxplot(width=0.1, fill="white", colour="black", alpha=0.5) +
  scale_y_log10(breaks=c(0.001, 0.01, 0.1, 1, 10, 100, 1000, 10000)) +
  labs(title="MRD MTM/mL | Clearance post-MRD", x="Clearance post-MRD", y="MRD MTM/mL") +
  theme_minimal() +
  theme(legend.position="none")
m3_1v2 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
                      data = circ_data[circ_data$ctDNA.Clearance %in% c("Sustained", "Transient"), ],
                      na.rm = TRUE)
print(m3_1v2)
m3_1v3 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
                      data = circ_data[circ_data$ctDNA.Clearance %in% c("Sustained", "No Clearance"), ],
                      na.rm = TRUE)
print(m3_1v3)
m3_2v3 <- wilcox.test(p_MRD_MTM ~ ctDNA.Clearance,
                      data = circ_data[circ_data$ctDNA.Clearance %in% c("Transient", "No Clearance"), ],
                      na.rm = TRUE)
print(m3_2v3)
```

#Percentages of recurred transient clearance that return positive
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data <- circ_data[circ_data$ACT=="TRUE",]
circ_data <- circ_data[circ_data$Clearance.Event=="TRUE",]
circ_data <- circ_data[circ_data$DFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_data <- subset(circ_data, !is.na(Transient.Clearance))
circ_data <- circ_data[circ_data$Transient.Clearance=="TRUE",]
circ_datadf <- as.data.frame(circ_data)

# Convert days to months
circ_data$p_drelReturned_months <- circ_data$p_drelReturned / 30.437

# Define the intervals: 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(3, 6, 9, 12, 15, 18, 21, 24, 27)
labels <- c("3-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")

# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$p_drelReturned_months, breaks = breaks, labels = labels, right = FALSE)

# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)

# Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)

# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)

# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)

# Print the summary
print(interval_summary)

# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)

# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages)

bp <- barplot(interval_percentages, 
        main="Distribution of ctDNA Intervals", 
        xlab="Intervals", 
        ylab="Percentage", 
        col="lightblue",
        ylim=c(0, 100),
        las=2) # las=2 makes the axis labels perpendicular to the axis


# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
```

#OS by ctDNA Clearance post-MRD - 3 Groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$Clearance.Cohort=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
surv_object <- Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)

survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Clearance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Clearance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
KM_curve <- survfit(surv_object ~ ctDNA.Clearance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue","green"), title="OS - ctDNA Clearance post-MRD | All Stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("No Clearance", "Sustained", "Transient"), legend.title="")
summary(KM_curve, times= c(12, 18, 24))
circ_data$ctDNA.Clearance <- as.factor(circ_data$ctDNA.Clearance)
circ_data$ctDNA.Clearance <- factor(circ_data$ctDNA.Clearance, levels=c("Sustained","Transient", "No Clearance"))
cox_fit <- coxphf(surv_object ~ ctDNA.Clearance, data=circ_data) 
summary(cox_fit)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Clearance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA Dynamics post-MRD", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#Percentages of MRD negative with molecular recurrence (returned positive) post-MRD
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)

# Convert days to months
#circ_data$PostMRDPos.months <- circ_data$PostMRDPos / 30.437

# Define the intervals: 0-6, 6-9, 9-12, 12-15, 15-18, 18-21, 21-24, >24 months
breaks <- c(0, 6, 9, 12, 15, 18, 21, 24, 48)
labels <- c("0-6m", "6-9m", "9-12m", "12-15m", "15-18m", "18-21m", "21-24m", ">24m")

# Categorize p_drelReturned_months into intervals
circ_data$p_drelReturned_intervals <- cut(circ_data$PostMRDPos.months, breaks = breaks, labels = labels, right = FALSE)

# Examine the distribution of the intervals
table(circ_data$p_drelReturned_intervals)

# Get the counts for each interval
interval_counts <- table(circ_data$p_drelReturned_intervals)

# Calculate the percentages
interval_percentages <- 100 * interval_counts / sum(interval_counts)

# Combine the counts and percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages)

# Calculate the total number of observations
total_observations <- sum(interval_counts)

# Add the total number of observations to the summary
interval_summary$TotalObservations <- c(rep(NA, length(interval_counts)-1), total_observations)

# Print the summary with total observations
print(interval_summary)

# Calculate cumulative percentages
cumulative_percentages <- cumsum(interval_percentages)

# Combine the counts, percentages, and cumulative percentages for a clearer overview
interval_summary <- data.frame(Counts = interval_counts, Percentages = interval_percentages, CumulativePercentages = cumulative_percentages, TotalObservations = c(rep(NA, length(interval_counts)-1), total_observations))

bp <- barplot(interval_percentages, 
              main="Distribution of ctDNA Intervals", 
              xlab="Intervals", 
              ylab="Percentage", 
              col="lightblue",
              ylim=c(0, 100),
              las=2) # las=2 makes the axis labels perpendicular to the axis

# Add the cumulative percentages to the plot
points(bp, cumulative_percentages, type="o", pch=22, col="red", cex=1.5)
print(interval_summary)
```




#OS by ctDNA MRD positive vs ctDNA negative with molecular recurrence at Surveillance - 3 groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics 
         = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","green","red"), title="OS - ctDNA MRD Pos vs Neg with Molecular Recurrence at Surveillance Window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"), legend.title="")
summary(KM_curve, times= c(12, 24))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("1","2","3"), labels = c("All-time negative","Molecular Recurrence", "ctDNA MRD Positive"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Molecular Recurrence at Surveillance Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))

rm(list=ls()) #repeat to compare Molecular Recurrence vs ctDNA MRD positive
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="NEGATIVE" ~ 1,
    ctDNA.MRD == "NEGATIVE" & ctDNA.Surveillance=="POSITIVE" ~ 2,
    ctDNA.MRD == "POSITIVE" ~ 3
  ))

circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","3","1"), labels = c("Molecular Recurrence", "ctDNA MRD Positive", "All-time negative"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
ggforest(cox_fit,data = circ_data) 
summary(cox_fit)
```

#Time-dependent analysis - Molecular Recurrence patients Landmark from molecular recurrence with RFS event as outcome
```{r}
rm(list=ls())
setwd("~/Downloads")
dt_final <- read.csv("Galaxy 36mo Time dependent.csv")
dt_final <- dt_final[!is.na(dt_final$tstart4), ]
dt_final$tstart4 <- as.numeric(as.character(dt_final$tstart4))
dt_final$tstop4 <- as.numeric(as.character(dt_final$tstop4))

datatable(dt_final, filter = "top")
fit <- coxph(Surv(tstart4, tstop4, rfs_event) ~ biomarker_status,
             data = dt_final)
summary(fit)
summary_fit <- summary(fit)
hr <- summary_fit$coef[1, "exp(coef)"]
ci_lower <- summary_fit$conf.int[1, "lower .95"]
ci_upper <- summary_fit$conf.int[1, "upper .95"]
p_value <- summary_fit$coef[1, "Pr(>|z|)"]
formatted_p_value <- ifelse(p_value < 0.0001, "<0.0001", sprintf("%.3f", p_value))
result_line <- sprintf("HR = %.2f (%.2f–%.2f); P %s", hr, ci_lower, ci_upper, formatted_p_value)
print(result_line)
```


#OS by timing of molecular recurrence in ctDNA MRD negative - 3 groups
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    PostMRDPos.months >= 0 & PostMRDPos.months < 6 ~ 1,
    PostMRDPos.months >= 6 & PostMRDPos.months < 12 ~ 2,
    PostMRDPos.months >= 12 & PostMRDPos.months < 24 ~ 3
  ))

circ_data <- circ_data[!is.na(circ_data$ctDNA.Dynamics),]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.Dynamics, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Dynamics) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Dynamics, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","green","blue"), title="OS - ctDNA MRD Neg with Molecular Recurrence", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("6mo","6-12mo", "12mo"), legend.title="")
summary(KM_curve, times= c(12, 24, 36))
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("3","2","1"), labels = c(">12 months","6-12 months", "<6 months"))
cox_fit <- coxphf(surv_object ~ ctDNA.Dynamics, data = circ_data, maxstep = 0.5, maxit = 100)
summary(cox_fit)

circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Dynamics, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Timing of Molecular Recurrence", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "" & circ_data$Lead.Time >= 0, ]
circ_data <- circ_data[circ_data$ctDNA.MRD=="NEGATIVE",]
circ_data <- circ_data[circ_data$PostMRDPos.Event=="TRUE",]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Dynamics <- NA #first we create the variable for the ctDNA & NAC combination, and we assign values
circ_data <- circ_data %>%
  mutate(ctDNA.Dynamics = case_when(
    PostMRDPos.months >= 0 & PostMRDPos.months < 6 ~ 1,
    PostMRDPos.months >= 6 & PostMRDPos.months < 12 ~ 2,
    PostMRDPos.months >= 12 & PostMRDPos.months < 24 ~ 3
  ))

circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
circ_data$ctDNA.Dynamics <- factor(circ_data$ctDNA.Dynamics, levels=c("2","1"), labels = c("6-12 months", "<6 months"))
cox_fit <- coxph(surv_object ~ ctDNA.Dynamics, data=circ_data) 
summary(cox_fit)
```

#DFS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA Surveillance window | All stages", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 1024)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the Surveillance Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```




#OS by ctDNA at the Surveillance Window - All stages Landmark 10 weeks
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - ctDNA Surveillance window | All stages", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)


circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the Surveillance Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```




#Multivariate cox regression at Surveillance Window for DFS - All stages Landmark 10 weeks
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$DFS.months=circ_data$DFS.months-2.5
circ_data <- circ_data[circ_data$DFS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"), labels = c("Negative", "Positive"))
circ_data$Gender <- factor(circ_data$Gender, levels = c("Female", "Male"))
circ_data$Age.Group <- factor(circ_data$Age.Group, levels = c("1", "2"), labels = c("<70", ">70"))
circ_data$PrimSite <- factor(circ_data$PrimSite, levels = c("Left-sided colon", "Right-sided colon"))
circ_data$ECOG <- factor(circ_data$ECOG, levels = c("0", "1"))
circ_data$pT <- factor(circ_data$pT, levels = c("T1-T2", "T3-T4"))
circ_data$pN <- factor(circ_data$pN, levels = c("N0", "N1-N2"))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
circ_data$BRAF.V600E <- factor(circ_data$BRAF.V600E, levels = c("WT", "MUT"), labels = c("Wild-Type", "V600E"))
circ_data$RAS <- factor(circ_data$RAS, levels = c("WT", "MUT"), labels = c("Wild-Type", "Mutant"))
surv_object <- Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event) 
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance + Gender + Age.Group + PrimSite + ECOG + pT + pN + MSI + BRAF.V600E + RAS, data=circ_data) 
ggforest(cox_fit, data = circ_data, main = "Multivariate Regression Model for DFS - All Stages", refLabel = "Reference Group")
test.ph <- cox.zph(cox_fit)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.SurveillancePositive", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 512)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))
```


#OS by ctDNA at the MRD Window - pts with Radiological Recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.MRD.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Radiological Recurrence | ctDNA MRD window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 36))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#OS by ctDNA at the MRD Window - pts with Radiological Recurrence Sites
```{r}
# Define the function to analyze each recurrence site and extract HR values
analyze_site <- function(site) {
  circ_data_site <- circ_data %>% filter(grepl(site, RelSite, ignore.case = TRUE))
  circ_data_site <- circ_data_site[circ_data_site$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
  
  surv_object <- Surv(time = circ_data_site$OS.MRD.months, event = circ_data_site$OS.Event)
  cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = circ_data_site) 
  cox_fit_summary <- summary(cox_fit)
  
  HR <- cox_fit_summary$coefficients[2]
  lower_CI <- cox_fit_summary$conf.int[3]
  upper_CI <- cox_fit_summary$conf.int[4]
  p_value <- cox_fit_summary$coefficients[5]
  
  label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", format.pval(p_value, digits = 3))
  return(list(HR = HR, lower_CI = lower_CI, upper_CI = upper_CI, p_value = p_value, site = site, label_text = label_text))
}

setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$RFS.Event == "TRUE",]
recurrence_sites <- c("liver", "lung", "peritoneum", "lymph node")
results <- lapply(recurrence_sites, analyze_site)
forest_data <- do.call(rbind, lapply(results, function(res) {
  data.frame(
    site = res$site,
    HR = res$HR,
    lower_CI = res$lower_CI,
    upper_CI = res$upper_CI,
    label_text = res$label_text
  )
}))

forest_data$site <- factor(forest_data$site, levels = c("liver", "lung", "peritoneum", "lymph node"))
forest_plot <- ggplot(forest_data, aes(x = site, y = HR, ymin = lower_CI, ymax = upper_CI)) +
  geom_pointrange() +
  geom_text(aes(label = label_text), hjust = -0.1, vjust = -0.5) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  coord_flip() +
  scale_y_continuous(breaks = seq(1, max(forest_data$upper_CI) + 1, by = 2), expand = c(0, 0), limits = c(0, max(forest_data$upper_CI) + 1)) +
  labs(x = "Recurrence Site", y = "HR for OS between ctDNA MRD positive vs negative") +
  theme_minimal()
# Define the function to analyze each recurrence site and extract HR values
analyze_site <- function(site) {
  circ_data_site <- circ_data %>% filter(grepl(site, RelSite, ignore.case = TRUE))
  circ_data_site <- circ_data_site[circ_data_site$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]

site_count <- nrow(circ_data_site)
  print(paste("Number of observations for site", site, ":", site_count))
  
  surv_object <- Surv(time = circ_data_site$OS.months, event = circ_data_site$OS.Event)
  cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = circ_data_site) 
  cox_fit_summary <- summary(cox_fit)
  
  HR <- cox_fit_summary$coefficients[2]
  lower_CI <- cox_fit_summary$conf.int[3]
  upper_CI <- cox_fit_summary$conf.int[4]
  p_value <- cox_fit_summary$coefficients[5]
  
  label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", format.pval(p_value, digits = 3))
  return(list(HR = HR, lower_CI = lower_CI, upper_CI = upper_CI, p_value = p_value, site = site, label_text = label_text))
}

# Set working directory and load data
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$RFS.Event == "TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]

# Recurrence sites to analyze
recurrence_sites <- c("liver", "lung", "peritoneum", "lymph node")

# Perform analysis for each site
results <- lapply(recurrence_sites, analyze_site)

# Create data frame for forest plot
forest_data <- do.call(rbind, lapply(results, function(res) {
  data.frame(
    site = res$site,
    HR = res$HR,
    lower_CI = res$lower_CI,
    upper_CI = res$upper_CI,
    label_text = res$label_text
  )
}))

# Set the order of the levels for the 'site' factor
forest_data$site <- factor(forest_data$site, levels = c("liver", "lung", "peritoneum", "lymph node"))

# Create forest plot
forest_plot <- ggplot(forest_data, aes(x = site, y = HR, ymin = lower_CI, ymax = upper_CI)) +
  geom_pointrange() +
  geom_text(aes(label = label_text), hjust = -0.1, vjust = -0.5) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  coord_flip() +
  scale_y_continuous(breaks = seq(1, max(forest_data$upper_CI) + 1, by = 2), expand = c(0, 0), limits = c(0, max(forest_data$upper_CI) + 1)) +
  labs(x = "Recurrence Site", y = "HR for OS between ctDNA MRD positive vs negative") +
  theme_minimal()

print(forest_plot)
for (res in results) {
  print(res$label_text)
}
```

#OS by ctDNA at the Surveillance Window - pts with Radiological Recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]
circ_data$OS.months=circ_data$OS.months-2.5
circ_data <- circ_data[circ_data$OS.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$OS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$OS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="OS - Radiological Recurrence | ctDNA Surveillance window", ylab= "Overall Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24, 36))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the Surveillance Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#Sankey plot for MRD and Surveillance dynamics
```{r}
##To run this commands, please visit: https://sankeymatic.com/build/
#ctDNA at the MRD Window [336] ctDNA + MRD window #E67272
#ctDNA at the MRD Window [1773] ctDNA - MRD window #87EA86
#ctDNA at the MRD Window [131] Not available ctDNA MRD window #808080
#ctDNA + MRD window [141] ctDNA + Surveillance window #E67272
#ctDNA + MRD window [70] ctDNA - Surveillance window #87EA86
#ctDNA + MRD window [125] Not available ctDNA Surveillance window #808080
#ctDNA - MRD window [159] ctDNA + Surveillance window #E67272
#ctDNA - MRD window [1294] ctDNA - Surveillance window #87EA86
#ctDNA - MRD window [320] Not available ctDNA Surveillance window #808080
#Not available ctDNA MRD window [13] ctDNA + Surveillance window #E67272
#Not available ctDNA MRD window [117] ctDNA - Surveillance window #87EA86
#Not available ctDNA MRD window [1] Not available ctDNA Surveillance window #808080
#ctDNA + Surveillance window [264] Radiological Recurrence #E67272
#ctDNA + Surveillance window [49] No Recurrence #87EA86
#ctDNA - Surveillance window [78] Radiological Recurrence #E67272
#ctDNA - Surveillance window [1403] No Recurrence #87EA86
#Not available ctDNA Surveillance window [158] Radiological Recurrence #E67272
#Not available ctDNA Surveillance window [288] No Recurrence #87EA86
```

#Percentage of ctDNA MRD Window positivity in pts undergoing post-recurrence curative surgery
```{r}
rm(list = ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>%
  filter(Eligible == "TRUE" & RFS.Event == "TRUE" & ctDNA.MRD != "")
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
positive_rate <- sum(circ_data$ctDNA.MRD == "Positive" & circ_data$PostRecurrenceSurgery == "TRUE") / sum(circ_data$ctDNA.MRD == "Positive")* 100
positive_ci <- binconf(sum(circ_data$ctDNA.MRD == "Positive" & circ_data$PostRecurrenceSurgery == "TRUE"),
                       sum(circ_data$ctDNA.MRD == "Positive"),
                       alpha = 0.05)[c(2, 3)] * 100
negative_rate <- sum(circ_data$ctDNA.MRD == "Negative" & circ_data$PostRecurrenceSurgery == "TRUE") / sum(circ_data$ctDNA.MRD == "Negative")* 100
negative_ci <-  binconf(sum(circ_data$ctDNA.MRD == "Negative" & circ_data$PostRecurrenceSurgery == "TRUE"),
                        sum(circ_data$ctDNA.MRD == "Negative"),
                        alpha = 0.05)[c(2, 3)] * 100
data <- data.frame(
  ctDNA.MRD = c("Positive", "Negative"),
  percentage = c(positive_rate, negative_rate),
  lower_ci = c(positive_ci[1], negative_ci[1]),
  upper_ci = c(positive_ci[2], negative_ci[2])
)
cross_tab <- table(circ_data$ctDNA.MRD, circ_data$PostRecurrenceSurgery)
chi_test <- chisq.test(cross_tab)
p_value <- format.pval(chi_test$p.value, digits = 3)
print(data)
print(cross_tab)
print(chi_test)
barplot <- ggplot(data, aes(x = ctDNA.MRD, y = percentage, fill = ctDNA.MRD)) +
  geom_bar(stat = "identity") +
  geom_errorbar(aes(ymin = lower_ci, ymax = upper_ci), width = 0.2) +
  geom_text(aes(label = paste0(round(percentage, 1), "%")), vjust = -0.5) +
  labs(
    x = "ctDNA status at the MRD status",
    y = "Proportion of patients undergoing 
    post-recurrence curative surgery",
    caption = paste("Chi-squared test p-value: ", p_value)
  ) +
  scale_y_continuous(expand = c(0, 0), limits = c(0, 50)) +
  scale_fill_manual(values = c("Negative" = "blue", "Positive" = "red")) +
  theme_minimal()
print(barplot)
```

#PRS by ctDNA at the MRD Window - pts with Radiological Recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

survfit(Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PRS - Radiological Recurrence | ctDNA MRD window", ylab= "Post-Recurrence Survival", xlab="Time from Radiological Recurrence (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$OS.MRD.months>=0,]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#PRS by ctDNA at the Surveillance Window - pts with Radiological Recurrence
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

survfit(Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)~ctDNA.Surveillance, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.Surveillance) %>%
  summarise(
    Total = n(),
    Events = sum(OS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$PRS.months, event = circ_data$OS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.Surveillance, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="PRS - Radiological Recurrence | ctDNA Surveillance window", ylab= "Post-Recurrence Survival", xlab="Time from Radiological Recurrence (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(24))
circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.Surveillance, data=circ_data) 
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.Surveillance!="",]

circ_data$ctDNA.Surveillance <- factor(circ_data$ctDNA.Surveillance, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$OS.Event <- factor(circ_data$OS.Event, levels = c("FALSE", "TRUE"), labels = c("Alive", "Deceased"))
contingency_table <- table(circ_data$ctDNA.Surveillance, circ_data$OS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the Surveillance Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
         fill = "Vital Status",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("Alive" = "blue", "Deceased" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#Detection ctDNA rates based on sites of relapse
```{r}
# Remove existing objects and set the working directory
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RFS.Event=="TRUE",]

# Create a table of counts for the "Rec.Site" variable
relsite_counts <- table(circ_data$Rec.Site)
relsite_df <- as.data.frame(relsite_counts)
names(relsite_df) <- c("RelSite", "Count")
circ_data_pos_mrd <- circ_data[circ_data$ctDNA.MRD=="POSITIVE",]
circ_data_pos_anytime <- circ_data[circ_data$ctDNA.anytime=="POSITIVE",]
pos_counts_mrd <- table(circ_data_pos_mrd$Rec.Site)
pos_counts_anytime <- table(circ_data_pos_anytime$Rec.Site)
relsite_df$MRDPos_Count <- ifelse(is.na(match(relsite_df$RelSite, names(pos_counts_mrd))), 0, pos_counts_mrd[match(relsite_df$RelSite, names(pos_counts_mrd))])
relsite_df$MRDPos_Count[is.na(relsite_df$MRDPos_Count)] <- 0
relsite_df$AnytimePos_Count <- ifelse(is.na(match(relsite_df$RelSite, names(pos_counts_anytime))), 0, pos_counts_anytime[match(relsite_df$RelSite, names(pos_counts_anytime))])
relsite_df$AnytimePos_Count[is.na(relsite_df$AnytimePos_Count)] <- 0
relsite_df$Percent <- (relsite_df$Count / sum(relsite_df$Count)) * 100
relsite_df$MRDPos_Percent <- (relsite_df$MRDPos_Count / relsite_df$Count) * 100
relsite_df$AnytimePos_Percent <- (relsite_df$AnytimePos_Count / relsite_df$Count) * 100
total_observations <- sum(relsite_df$Count)
total_pos_mrd <- sum(relsite_df$MRDPos_Count)
total_pos_anytime <- sum(relsite_df$AnytimePos_Count)
total_row <- data.frame(RelSite = "Total", Count = total_observations, MRDPos_Count = total_pos_mrd, AnytimePos_Count = total_pos_anytime, Percent = 100, MRDPos_Percent = (total_pos_mrd / total_observations) * 100, AnytimePos_Percent = (total_pos_anytime / total_observations) * 100)
relsite_df <- rbind(relsite_df, total_row)
print(relsite_df)

ft <- flextable(relsite_df)
doc <- read_docx() %>%
  body_add_flextable(value = ft)
print(doc, target = "relsite_df.docx")
```


#Heatmap for Biomarkers factors
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data %>% arrange(RAS.BRAF)
circ_data$RAS <- factor(circ_data$RAS.BRAF, levels = c("TRUE", "FALSE"))
circ_datadf <- as.data.frame(circ_data)

ha <- HeatmapAnnotation(
  RAS.BRAF = circ_data$RAS.BRAF,
  TMB = circ_data$TMB,
  MSI = circ_data$MSI,
  BRAF.V600E = circ_data$BRAF.V600E,
  KRAS.G12C = circ_data$KRAS.G12C,
  ERBB2 = circ_data$ERBB2,
  TP53.Y220C = circ_data$TP53.Y220C,
  NTRK = circ_data$NTRK,
  RET = circ_data$RET,
  
    col = list(RAS.BRAF = c("TRUE" = "blue","FALSE" = "grey"),
    TMB = c("TMB-High" = "blue" , "TMB-Low" = "grey"),
    MSI = c("MSI-High" = "blue" , "MSS" = "grey"),
    BRAF.V600E = c("MUT" = "blue", "WT" = "grey"),
    KRAS.G12C = c("MUT" = "blue", "WT" = "grey"),
    ERBB2 = c("MUT" = "blue", "WT" = "grey"),
    TP53.Y220C = c("MUT" = "blue", "WT" = "grey"),
    NTRK = c("MUT" = "blue", "WT" = "grey"),
    RET = c("MUT" = "blue", "WT" = "grey")))
ht <- Heatmap(matrix(nrow = 0, ncol = length(circ_data$RAS.BRAF)),show_row_names = FALSE,cluster_rows = F,cluster_columns = FALSE, top_annotation = ha)
pdf("heatmap.pdf",width = 7, height = 7)
draw(ht, annotation_legend_side = "bottom")
dev.off()
```


#Calculate the % altered variables
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
conditions <- list(
  RAS.BRAF = "TRUE",
  TMB = "TMB-High",
  MSI = "MSI-High",
  BRAF.V600E = "MUT",
  KRAS.G12C = "MUT",
  ERBB2 = "MUT",
  TP53.Y220C = "MUT",
  NTRK = "MUT",
  RET = "MUT"
)
total_observations <- nrow(circ_data)
condition_counts <- list()
for (var in names(conditions)) {
  condition_value <- conditions[[var]]
  condition_count <- sum(circ_data[[var]] == condition_value, na.rm = TRUE)
  condition_percentage <- (condition_count / total_observations) * 100
  condition_counts[[var]] <- list('Count' = condition_count, 'Percentage' = condition_percentage)
}
condition_counts_df <- do.call(rbind, lapply(names(condition_counts), function(x) {
  data.frame(Variable = x, 
             Count = condition_counts[[x]]$Count, 
             Percentage = condition_counts[[x]]$Percentage)
}))
print(condition_counts_df)
```


#DFS by Biomarkers
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data %>% 
  mutate(
    RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
    TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
    MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
    BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
    KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
    ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
    TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
  )
circ_data_long <- circ_data %>%
  gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
  filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))

survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)
event_summary <- circ_data_long %>%
  group_by(value) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)
KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
  KM_curve, 
  data = circ_data_long,
  risk.table = TRUE,
  pval = FALSE,
  conf.int = FALSE,
  break.time.by = 6,
  xlab = "Time from surgery (months)",
  ylab = "Disease-free Survival",
  legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
  palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))

circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Biomarkers", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```


#DFS by Biomarkers - Stage I-III
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data[!(circ_data$Stage %in% c("IV")),]
circ_data <- circ_data %>% 
  mutate(
    RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
    TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
    MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
    BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
    KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
    ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
    TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
  )
circ_data_long <- circ_data %>%
  gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
  filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))

survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)
event_summary <- circ_data_long %>%
  group_by(value) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)
KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
  KM_curve, 
  data = circ_data_long,
  risk.table = TRUE,
  pval = FALSE,
  conf.int = FALSE,
  break.time.by = 6,
  xlab = "Time from surgery (months)",
  ylab = "Disease-free Survival",
  legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
  palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))

circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Biomarkers", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#DFS by Biomarkers - Stage IV
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data[!(circ_data$Stage %in% c("I", "II", "III")),]
circ_data <- circ_data %>% 
  mutate(
    RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
    TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
    MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
    BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
    KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
    ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
    TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
  )
circ_data_long <- circ_data %>%
  gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
  filter(!is.na(value))
circ_data_long$value <- factor(circ_data_long$value, levels = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))

survfit(Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)~value, data = circ_data_long)
event_summary <- circ_data_long %>%
  group_by(value) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_obj <- Surv(time = circ_data_long$DFS.months, event = circ_data_long$DFS.Event)
cox_model <- coxph(surv_obj ~ value, data = circ_data_long)
summary(cox_model)
KM_curve <- survfit(surv_obj ~ value, data = circ_data_long)
ggsurvplot(
  KM_curve, 
  data = circ_data_long,
  risk.table = TRUE,
  pval = FALSE,
  conf.int = FALSE,
  break.time.by = 6,
  xlab = "Time from surgery (months)",
  ylab = "Disease-free Survival",
  legend.labs = c("RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
  palette = c("red", "purple", "green", "blue", "orange", "skyblue", "cyan")
)
summary(KM_curve, times = c(24))

circ_data_long$DFS.Event <- factor(circ_data_long$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data_long$value, circ_data_long$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "Biomarkers", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```


#DFS by MSI status - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~MSI, data = circ_data)
event_summary <- circ_data %>%
  group_by(MSI) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ MSI, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - MSI Status | All stages", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("MSS", "MSI-High"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$MSI <- factor(circ_data$MSI, levels = c("MSS", "MSI-High"), labels = c("MSS", "MSI-High"))
cox_fit <- coxph(surv_object ~ MSI, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "MSI Status", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```




#DFS by TMB status - All stages
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_datadf <- as.data.frame(circ_data)
circ_data$TMB <- factor(circ_data$TMB, levels = c("TMB-Low", "TMB-High"), labels = c("TMB-Low", "TMB-High"))

survfit(Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)~MSI, data = circ_data)
event_summary <- circ_data %>%
  group_by(TMB) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ TMB, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("red","blue"), title="DFS - TMB Status | All stages", ylab= "Disease-Free Survival", xlab="Time from Surgery (Months)", legend.labs=c("TMB-Low", "TMB-High"), legend.title="")
summary(KM_curve, times= c(24, 30, 36))
circ_data$TMB <- factor(circ_data$TMB, levels = c("TMB-Low", "TMB-High"), labels = c("TMB-Low", "TMB-High"))
cox_fit <- coxph(surv_object ~ TMB, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$TMB, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "TMB Status", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```




#Percentage of ctDNA MRD Window positivity in biomarker groups
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data %>% filter(Eligible == "TRUE")
circ_data <- circ_data %>% 
  mutate(
    RAS.BRAF = ifelse(RAS.BRAF == "TRUE", "RAS/BRAF WT", NA),
    TMB = ifelse(TMB == "TMB-High", "TMB High", NA),
    MSI = ifelse(MSI == "MSI-High", "MSI High", NA),
    BRAF.V600E = ifelse(BRAF.V600E == "MUT", "BRAF V600E", NA),
    KRAS.G12C = ifelse(KRAS.G12C == "MUT", "KRAS G12C", NA),
    ERBB2 = ifelse(ERBB2 == "MUT", "ERBB2", NA),
    TP53.Y220C = ifelse(TP53.Y220C == "MUT", "TP53 Y220C", NA)
  )
circ_data_long <- circ_data %>%
  gather(key = "group", value = "value", RAS.BRAF, TMB, MSI, BRAF.V600E, KRAS.G12C, ERBB2, TP53.Y220C) %>%
  filter(!is.na(value))

summary_data <- circ_data_long %>%
  group_by(value) %>%
  summarise(
    n = n(),
    positive = sum(ctDNA.MRD == "POSITIVE"),
    pct_positive = (positive / n) * 100,
    se = sqrt((pct_positive / 100) * (1 - pct_positive / 100) / n),
    ci_low = pct_positive - 1.96 * se * 100,
    ci_high = pct_positive + 1.96 * se * 100
  )

overall_summary <- circ_data_long %>%
  summarise(
    value = "Overall",
    n = n(),
    positive = sum(ctDNA.MRD == "POSITIVE"),
    pct_positive = (positive / n) * 100,
    se = sqrt((pct_positive / 100) * (1 - pct_positive / 100) / n),
    ci_low = pct_positive - 1.96 * se * 100,
    ci_high = pct_positive + 1.96 * se * 100
  )

summary_data <- bind_rows(overall_summary, summary_data)

summary_data$value <- factor(summary_data$value, levels = c("Overall", "RAS/BRAF WT", "TMB High", "MSI High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"))
ggplot(summary_data, aes(x = value, y = pct_positive)) +
  geom_bar(stat = "identity", fill = "blue", alpha = 0.7) +
  geom_errorbar(aes(ymin = ci_low, ymax = ci_high), width = 0.2) +
  geom_text(aes(label = sprintf("%.1f%%", pct_positive)), vjust = -0.5, color = "black") +
  labs(
    x = "Genetic Mutation",
    y = "Post-surgical MRD positivity %"
  ) +
  theme(
    panel.background = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.line = element_line(color = "black"),
    axis.ticks = element_line(color = "black"),
    axis.text.x = element_text(angle = 45, hjust = 1),
    plot.background = element_blank())
```


#DFS by ctDNA at the MRD Window - BRAF V600E Landmark MRD timepoint
```{r}
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$BRAF.V600E=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~ctDNA.MRD, data = circ_data)
event_summary <- circ_data %>%
  group_by(ctDNA.MRD) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, break.time.by=6, palette=c("blue","red"), title="DFS - ctDNA MRD window | BRAF V600E", ylab= "Disease-Free Survival", xlab="Time from Landmark Time point (Months)", legend.labs=c("ctDNA Negative", "ctDNA Positive"), legend.title="")
summary(KM_curve, times= c(0, 24))
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

# Extract values for HR, 95% CI, and p-value
HR <- cox_fit_summary$coefficients[2]
lower_CI <- cox_fit_summary$conf.int[3]
upper_CI <- cox_fit_summary$conf.int[4]
p_value <- cox_fit_summary$coefficients[5]
label_text <- paste0("HR = ", round(HR, 2), " (", round(lower_CI, 2), "-", round(upper_CI, 2), "); p = ", round(p_value, 3))
print(label_text)

rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$BRAF.V600E=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]
circ_datadf <- as.data.frame(circ_data)

circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels = c("NEGATIVE", "POSITIVE"), labels = c("Negative", "Positive"))
circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$ctDNA.MRD, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size

#Calculate the exact p-value from Chi-square test
p_value <- chi_square_test$p.value
p_value_mpfr <- mpfr(p_value, precBits = 256)
print(paste("Exact p-value with high precision:", format(p_value_mpfr, scientific = TRUE)))
```




#DFS by ctDNA at the MRD Window - Forest plot with all subgroups of biomarkers
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$DFS.MRD.months >= 0,]
perform_cox <- function(data, filter_col = NULL, filter_val = NULL) {
  if (!is.null(filter_col) & !is.null(filter_val)) {
    data <- data[data[[filter_col]] == filter_val,]
  }
  surv_object <- Surv(time = data$DFS.MRD.months, event = data$DFS.Event)
  cox_fit <- coxph(surv_object ~ ctDNA.MRD, data = data)
  cox_fit_summary <- summary(cox_fit)
  HR <- cox_fit_summary$coefficients[2]
  lower_CI <- cox_fit_summary$conf.int[3]
  upper_CI <- cox_fit_summary$conf.int[4]
  p_value <- cox_fit_summary$coefficients[5]
  return(c(HR, lower_CI, upper_CI, p_value))
}

results <- data.frame(
  Subgroup = c("All", "RAS/BRAF WT", "TMB-High", "MSI-High", "BRAF V600E", "KRAS G12C", "ERBB2", "TP53 Y220C"),
  HR = rep(NA, 8),
  lower_CI = rep(NA, 8),
  upper_CI = rep(NA, 8),
  p_value = rep(NA, 8)
)

results[1, 2:5] <- perform_cox(circ_data)
results[2, 2:5] <- perform_cox(circ_data, "RAS.BRAF", "TRUE")
results[3, 2:5] <- perform_cox(circ_data, "TMB", "TMB-High")
results[4, 2:5] <- perform_cox(circ_data, "MSI", "MSI-High")
results[5, 2:5] <- perform_cox(circ_data, "BRAF.V600E", "MUT")
results[6, 2:5] <- perform_cox(circ_data, "KRAS.G12C", "MUT")
results[7, 2:5] <- perform_cox(circ_data, "ERBB2", "MUT")
results[8, 2:5] <- perform_cox(circ_data, "TP53.Y220C", "MUT")

results$HR <- as.numeric(results$HR)
results$lower_CI <- as.numeric(results$lower_CI)
results$upper_CI <- as.numeric(results$upper_CI)
results$p_value <- as.numeric(results$p_value)
results$label_text <- paste0(
  "HR = ", round(results$HR, 2), 
  "\n95% CI = ", round(results$lower_CI, 2), "-", round(results$upper_CI, 2),
  "\np = ", round(results$p_value, 3)
)
ggplot(results, aes(x = Subgroup, y = HR)) +
  geom_point(size = 3) +
  geom_errorbar(aes(ymin = lower_CI, ymax = upper_CI), width = 0.2) +
  geom_text(aes(label = label_text), hjust = -0.2, vjust = 0.5, size = 3.5) +
  scale_y_log10() +
  geom_hline(yintercept = 1, linetype = "dashed") +
  labs(title = "Forest Plot of HR for DFS between ctDNA Positive versus Negative",
       x = "Subgroup",
       y = "Hazard Ratio (HR)") +
  coord_flip() +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```


#DFS by ctDNA at the MRD Window - individual cox regression models for each biomarker to extract the exact p value
```{r}
#ERBB2 Amplification
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$ERBB2=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#KRAS G12C Amplification
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$KRAS.G12C=="MUT",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#MSI-High
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$MSI=="MSI-High",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#TMB-High
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$TMB=="TMB-High",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#RAS/BRAF WT
rm(list=ls())
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible=="TRUE",]
circ_data <- circ_data[circ_data$RAS.BRAF=="TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD!="",]
circ_data <- circ_data[circ_data$DFS.MRD.months>=0,]

surv_object <-Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ ctDNA.MRD, data = circ_data,conf.int=0.95,conf.type="log-log") 
circ_data$ctDNA.MRD <- factor(circ_data$ctDNA.MRD, levels=c("NEGATIVE","POSITIVE"))
cox_fit <- coxph(surv_object ~ ctDNA.MRD, data=circ_data) 
ggforest(cox_fit,data = circ_data)
summary(cox_fit)
cox_fit_summary <- summary(cox_fit)

#Calculate the exact p value for ctDNA.MRD
z_value <- cox_fit_summary$coefficients["ctDNA.MRDPOSITIVE", "z"]
z_value_mpfr <- mpfr(abs(z_value), precBits = 256)
p_value_mpfr <- 2 * (1 - pnorm(z_value_mpfr))
print(paste("Exact p-value for ctDNA.MRD:", format(p_value_mpfr, scientific = TRUE)))
```

#DFS by BRAF & MSI - ctDNA Positive Landmark MRD timepoint
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$ctDNA.MRD == "POSITIVE",]
circ_data <- circ_data[circ_data$DFS.MRD.months >= 0,]

# Create the BRAF.MSI variable
circ_data$BRAF.MSI <- NA
circ_data <- circ_data %>%
  mutate(BRAF.MSI = case_when(
    BRAF.V600E == "WT" & MSI == "MSS" ~ 1,
    BRAF.V600E == "WT" & MSI == "MSI-High" ~ 2,
    BRAF.V600E == "MUT" & MSI == "MSI-High" ~ 3,
    BRAF.V600E == "MUT" & MSI == "MSS" ~ 4
  ))

circ_data$BRAF.MSI <- factor(circ_data$BRAF.MSI, levels = c(1, 2, 3, 4), 
                             labels = c("BRAF WT & MSS", "BRAF WT & MSI-High", 
                                        "BRAF V600E & MSI-High", "BRAF V600E & MSS"))

print(table(circ_data$BRAF.MSI, useNA = "ifany"))
circ_data <- circ_data[!is.na(circ_data$BRAF.MSI),]
if(nrow(circ_data) == 0) {
  stop("No non-missing observations in the dataset after filtering.")
}
survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~BRAF.MSI, data = circ_data)
event_summary <- circ_data %>%
  group_by(BRAF.MSI) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95, conf.type = "log-log")

# Plot the Kaplan-Meier curve
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 6, palette = c("blue", "green", "purple", "red"), 
           title = "DFS - BRAF & MSI | ctDNA MRD Positive", ylab = "Disease-Free Survival", 
           xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("BRAF WT & MSS", "BRAF WT & MSI-High", 
                           "BRAF V600E & MSI-High", "BRAF V600E & MSS"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
cox_fit <- coxph(surv_object ~ BRAF.MSI, data = circ_data)
summary(cox_fit)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$BRAF.MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

#DFS by BRAF & MSI - ctDNA Negative Landmark MRD timepoint
```{r}
setwd("~/Downloads")
circ_data <- read.csv("Galaxy Data_20240603 Complete Dataset.csv")
circ_data <- circ_data[circ_data$Eligible == "TRUE",]
circ_data <- circ_data[circ_data$ctDNA.MRD != "",]
circ_data <- circ_data[circ_data$ctDNA.MRD == "NEGATIVE",]
circ_data <- circ_data[circ_data$DFS.MRD.months > 0,]

circ_data$BRAF.MSI <- NA
circ_data <- circ_data %>%
  mutate(BRAF.MSI = case_when(
    BRAF.V600E == "WT" & MSI == "MSS" ~ 1,
    BRAF.V600E == "WT" & MSI == "MSI-High" ~ 2,
    BRAF.V600E == "MUT" & MSI == "MSI-High" ~ 3,
    BRAF.V600E == "MUT" & MSI == "MSS" ~ 4
  ))

circ_data$BRAF.MSI <- factor(circ_data$BRAF.MSI, levels = c(1, 2, 3, 4), 
                             labels = c("BRAF WT & MSS", "BRAF WT & MSI-High", 
                                        "BRAF V600E & MSI-High", "BRAF V600E & MSS"))
print(table(circ_data$BRAF.MSI, useNA = "ifany"))
circ_data <- circ_data[!is.na(circ_data$BRAF.MSI),]
if (any(!is.finite(circ_data$DFS.MRD.months)) || any(!is.finite(circ_data$DFS.Event))) {
  stop("Data contains non-finite values.")
}
if (nrow(circ_data) == 0) {
  stop("No non-missing observations in the dataset after filtering.")
}

survfit(Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)~BRAF.MSI, data = circ_data)
event_summary <- circ_data %>%
  group_by(BRAF.MSI) %>%
  summarise(
    Total = n(),
    Events = sum(DFS.Event),
    Fraction = Events / n(),
    Percentage = (Events / n()) * 100
  )
print(event_summary)
surv_object <- Surv(time = circ_data$DFS.MRD.months, event = circ_data$DFS.Event)
KM_curve <- survfit(surv_object ~ BRAF.MSI, data = circ_data, conf.int = 0.95, conf.type = "log-log")

# Plot the Kaplan-Meier curve
ggsurvplot(KM_curve, data = circ_data, pval = FALSE, conf.int = FALSE, risk.table = TRUE, 
           break.time.by = 6, palette = c("blue", "green", "purple", "red"), 
           title = "DFS - BRAF & MSI | ctDNA MRD Negative", ylab = "Disease-Free Survival", 
           xlab = "Time from Landmark Time point (Months)", 
           legend.labs = c("BRAF WT & MSS", "BRAF WT & MSI-High", 
                           "BRAF V600E & MSI-High", "BRAF V600E & MSS"), 
           legend.title = "")
summary(KM_curve, times = c(0, 24))
cox_fit <- coxphf(surv_object ~ BRAF.MSI, data = circ_data)
summary(cox_fit)

circ_data$DFS.Event <- factor(circ_data$DFS.Event, levels = c("FALSE", "TRUE"), labels = c("No Recurrence", "Recurrence"))
contingency_table <- table(circ_data$BRAF.MSI, circ_data$DFS.Event)
chi_square_test <- chisq.test(contingency_table)
print(chi_square_test)
fisher_exact_test <- fisher.test(contingency_table)
print(fisher_exact_test)
print(contingency_table)
table_df <- as.data.frame(contingency_table)
table_df$Total <- ave(table_df$Freq, table_df$Var1, FUN = sum)
table_df$Percentage <- table_df$Freq / table_df$Total
table_df$MiddlePercentage <- table_df$Percentage / 2
ggplot(table_df, aes(x = Var1, y = Percentage, fill = Var2)) +
  geom_bar(stat = "identity") +
  geom_text(aes(y = MiddlePercentage, label = Freq), position = "stack", color = "black", vjust = 1.5, size = 7) +
  theme_minimal() +
  labs(title = "ctDNA at the MRD Window", 
       x = "ctDNA", 
       y = "Patients (%)", 
       fill = "Recurrence",
       caption = paste("Chi-squared test p-value: ", format.pval(chi_square_test$p.value))) +
  scale_y_continuous(labels = scales::percent_format()) +
  scale_fill_manual(values = c("No Recurrence" = "blue", "Recurrence" = "red")) + # define custom colors
  theme(axis.text.x = element_text(angle = 0, hjust = 1.5, size = 14), # increase x-axis text size
        axis.text.y = element_text(size = 14, color = "black"), # increase y-axis text size
        axis.title.x = element_text(size = 14, color = "black"), # increase x-axis label size
        axis.title.y = element_text(size = 14, color = "black"), # increase y-axis label size
        legend.text = element_text(size = 12, color = "black"))  # increase Recurrence label size
```

